{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "010c0e2e",
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2024-11-09T22:29:10.604119Z",
     "iopub.status.busy": "2024-11-09T22:29:10.603408Z",
     "iopub.status.idle": "2024-11-09T22:29:23.493210Z",
     "shell.execute_reply": "2024-11-09T22:29:23.492043Z"
    },
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     "duration": 12.898724,
     "end_time": "2024-11-09T22:29:23.495692",
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     "start_time": "2024-11-09T22:29:10.596968",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "! pip install -q umap-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81bbde36",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2024-11-09T22:29:23.506549Z",
     "iopub.status.busy": "2024-11-09T22:29:23.505976Z",
     "iopub.status.idle": "2024-11-09T22:29:23.514661Z",
     "shell.execute_reply": "2024-11-09T22:29:23.513768Z"
    },
    "papermill": {
     "duration": 0.016095,
     "end_time": "2024-11-09T22:29:23.516581",
     "exception": false,
     "start_time": "2024-11-09T22:29:23.500486",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing loss.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile loss.py\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class LabelDifference(nn.Module):\n",
    "    def __init__(self, distance_type=\"l1\"):\n",
    "        super(LabelDifference, self).__init__()\n",
    "        self.distance_type = distance_type\n",
    "\n",
    "    def forward(self, labels):\n",
    "        # labels: [bs, label_dim]\n",
    "        # output: [bs, bs]\n",
    "        if self.distance_type == \"l1\":\n",
    "            return torch.abs(labels[:, None, :] - labels[None, :, :]).sum(dim=-1)\n",
    "        else:\n",
    "            raise ValueError(self.distance_type)\n",
    "\n",
    "\n",
    "class FeatureSimilarity(nn.Module):\n",
    "    def __init__(self, similarity_type=\"l2\"):\n",
    "        super(FeatureSimilarity, self).__init__()\n",
    "        self.similarity_type = similarity_type\n",
    "\n",
    "    def forward(self, features):\n",
    "        # labels: [bs, feat_dim]\n",
    "        # output: [bs, bs]\n",
    "        if self.similarity_type == \"l2\":\n",
    "            return -(features[:, None, :] - features[None, :, :]).norm(2, dim=-1)\n",
    "        else:\n",
    "            raise ValueError(self.similarity_type)\n",
    "\n",
    "\n",
    "class RnCLoss(nn.Module):\n",
    "    def __init__(self, temperature=2, label_diff=\"l1\", feature_sim=\"l2\"):\n",
    "        super(RnCLoss, self).__init__()\n",
    "        self.t = temperature\n",
    "        self.label_diff_fn = LabelDifference(label_diff)\n",
    "        self.feature_sim_fn = FeatureSimilarity(feature_sim)\n",
    "\n",
    "    def forward(self, features, labels):\n",
    "        # features: [bs, 2, feat_dim]\n",
    "        # labels: [bs, label_dim]\n",
    "\n",
    "        features = torch.cat([features[:, 0], features[:, 1]], dim=0)  # [2bs, feat_dim]\n",
    "        labels = labels.repeat(2, 1)  # [2bs, label_dim]\n",
    "\n",
    "        label_diffs = self.label_diff_fn(labels)\n",
    "        logits = self.feature_sim_fn(features).div(self.t)\n",
    "        logits_max, _ = torch.max(logits, dim=1, keepdim=True)\n",
    "        logits -= logits_max.detach()\n",
    "        exp_logits = logits.exp()\n",
    "\n",
    "        n = logits.shape[0]  # n = 2bs\n",
    "\n",
    "        # remove diagonal\n",
    "        logits = logits.masked_select((1 - torch.eye(n).to(logits.device)).bool()).view(\n",
    "            n, n - 1\n",
    "        )\n",
    "        exp_logits = exp_logits.masked_select(\n",
    "            (1 - torch.eye(n).to(logits.device)).bool()\n",
    "        ).view(n, n - 1)\n",
    "        label_diffs = label_diffs.masked_select(\n",
    "            (1 - torch.eye(n).to(logits.device)).bool()\n",
    "        ).view(n, n - 1)\n",
    "\n",
    "        loss = 0.0\n",
    "        for k in range(n - 1):\n",
    "            pos_logits = logits[:, k]  # 2bs\n",
    "            pos_label_diffs = label_diffs[:, k]  # 2bs\n",
    "            neg_mask = (\n",
    "                label_diffs >= pos_label_diffs.view(-1, 1)\n",
    "            ).float()  # [2bs, 2bs - 1]\n",
    "            pos_log_probs = pos_logits - torch.log(\n",
    "                (neg_mask * exp_logits).sum(dim=-1)\n",
    "            )  # 2bs\n",
    "            loss += -(pos_log_probs / (n * (n - 1))).sum()\n",
    "\n",
    "        return loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "87967e0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-09T22:29:23.526559Z",
     "iopub.status.busy": "2024-11-09T22:29:23.526272Z",
     "iopub.status.idle": "2024-11-09T22:29:23.534482Z",
     "shell.execute_reply": "2024-11-09T22:29:23.533623Z"
    },
    "papermill": {
     "duration": 0.015433,
     "end_time": "2024-11-09T22:29:23.536362",
     "exception": false,
     "start_time": "2024-11-09T22:29:23.520929",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing convnext.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile convnext.py\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from timm.layers import DropPath, trunc_normal_\n",
    "\n",
    "\n",
    "class Block(nn.Module):\n",
    "    def __init__(self, dim, drop_path=0.0, layer_scale_init_value=1e-6):\n",
    "        super().__init__()\n",
    "        self.dwconv = nn.Conv1d(\n",
    "            dim, dim, kernel_size=7, padding=3, groups=dim\n",
    "        )  # depthwise conv\n",
    "        self.norm = LayerNorm(dim, eps=1e-6)\n",
    "        self.pwconv1 = nn.Linear(\n",
    "            dim, 4 * dim\n",
    "        )  # pointwise conv, implemented with linear layers\n",
    "        self.act = nn.GELU()\n",
    "        self.pwconv2 = nn.Linear(4 * dim, dim)\n",
    "        self.gamma = (\n",
    "            nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)\n",
    "            if layer_scale_init_value > 0\n",
    "            else None\n",
    "        )\n",
    "        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n",
    "\n",
    "    def forward(self, x):\n",
    "        input = x\n",
    "        x = self.dwconv(x)\n",
    "        x = x.permute(0, 2, 1)  # (N, C, W) -> (N, W, C)\n",
    "        x = self.norm(x)\n",
    "        x = self.pwconv1(x)\n",
    "        x = self.act(x)\n",
    "        x = self.pwconv2(x)\n",
    "        if self.gamma is not None:\n",
    "            x = self.gamma * x\n",
    "        x = x.permute(0, 2, 1)  # (N, W, C) -> (N, C, W)\n",
    "        x = input + self.drop_path(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class ConvNeXt(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        in_chans=1,\n",
    "        num_classes=1000,\n",
    "        depths=[3, 3, 9, 3],\n",
    "        dims=[96, 192, 384, 768],\n",
    "        drop_path_rate=0.0,\n",
    "        layer_scale_init_value=1e-6,\n",
    "        head_init_scale=1.0,\n",
    "    ):\n",
    "        super().__init__()\n",
    "\n",
    "        self.downsample_layers = (\n",
    "            nn.ModuleList()\n",
    "        )  # stem and 3 intermediate downsampling conv layers\n",
    "        stem = nn.Sequential(\n",
    "            nn.Conv1d(in_chans, dims[0], kernel_size=4, stride=4),\n",
    "            LayerNorm(dims[0], eps=1e-6, data_format=\"channels_first\"),\n",
    "        )\n",
    "        self.downsample_layers.append(stem)\n",
    "        for i in range(3):\n",
    "            downsample_layer = nn.Sequential(\n",
    "                LayerNorm(dims[i], eps=1e-6, data_format=\"channels_first\"),\n",
    "                nn.Conv1d(dims[i], dims[i + 1], kernel_size=2, stride=2),\n",
    "            )\n",
    "            self.downsample_layers.append(downsample_layer)\n",
    "\n",
    "        self.stages = (\n",
    "            nn.ModuleList()\n",
    "        )  # 4 feature resolution stages, each consisting of multiple residual blocks\n",
    "        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]\n",
    "        cur = 0\n",
    "        for i in range(4):\n",
    "            stage = nn.Sequential(\n",
    "                *[\n",
    "                    Block(\n",
    "                        dim=dims[i],\n",
    "                        drop_path=dp_rates[cur + j],\n",
    "                        layer_scale_init_value=layer_scale_init_value,\n",
    "                    )\n",
    "                    for j in range(depths[i])\n",
    "                ]\n",
    "            )\n",
    "            self.stages.append(stage)\n",
    "            cur += depths[i]\n",
    "\n",
    "        self.norm = nn.LayerNorm(dims[-1], eps=1e-6)  # final norm layer\n",
    "        self.head = nn.Linear(dims[-1], num_classes)\n",
    "\n",
    "        self.apply(self._init_weights)\n",
    "        self.head.weight.data.mul_(head_init_scale)\n",
    "        self.head.bias.data.mul_(head_init_scale)\n",
    "\n",
    "    def _init_weights(self, m):\n",
    "        if isinstance(m, (nn.Conv1d, nn.Linear)):\n",
    "            trunc_normal_(m.weight, std=0.02)\n",
    "            nn.init.constant_(m.bias, 0)\n",
    "\n",
    "    def forward_features(self, x):\n",
    "        for i in range(4):\n",
    "            x = self.downsample_layers[i](x)\n",
    "            x = self.stages[i](x)\n",
    "        return self.norm(x.mean(-1))  # global average pooling, (N, C, W) -> (N, C)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.forward_features(x)\n",
    "        x = self.head(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class LayerNorm(nn.Module):\n",
    "    def __init__(self, normalized_shape, eps=1e-6, data_format=\"channels_last\"):\n",
    "        super().__init__()\n",
    "        self.weight = nn.Parameter(torch.ones(normalized_shape))\n",
    "        self.bias = nn.Parameter(torch.zeros(normalized_shape))\n",
    "        self.eps = eps\n",
    "        self.data_format = data_format\n",
    "        if self.data_format not in [\"channels_last\", \"channels_first\"]:\n",
    "            raise NotImplementedError\n",
    "        self.normalized_shape = (normalized_shape,)\n",
    "\n",
    "    def forward(self, x):\n",
    "        if self.data_format == \"channels_last\":\n",
    "            return F.layer_norm(\n",
    "                x, self.normalized_shape, self.weight, self.bias, self.eps\n",
    "            )\n",
    "        elif self.data_format == \"channels_first\":\n",
    "            u = x.mean(1, keepdim=True)\n",
    "            s = (x - u).pow(2).mean(1, keepdim=True)\n",
    "            x = (x - u) / torch.sqrt(s + self.eps)\n",
    "            x = self.weight[:, None] * x + self.bias[:, None]\n",
    "            return x\n",
    "\n",
    "\n",
    "def convnext_tiny(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def convnext_small(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def convnext_base(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def convnext_large(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def convnext_xlarge(**kwargs):\n",
    "    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25e3ac6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-09T22:29:23.546104Z",
     "iopub.status.busy": "2024-11-09T22:29:23.545828Z",
     "iopub.status.idle": "2024-11-09T22:29:23.552416Z",
     "shell.execute_reply": "2024-11-09T22:29:23.551497Z"
    },
    "papermill": {
     "duration": 0.013607,
     "end_time": "2024-11-09T22:29:23.554286",
     "exception": false,
     "start_time": "2024-11-09T22:29:23.540679",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing ppg_dalia.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile ppg_dalia.py\n",
    "import glob\n",
    "import os\n",
    "\n",
    "import mne\n",
    "import numpy as np\n",
    "import torch\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch.utils.data import Dataset\n",
    "\n",
    "data_dir = \"/kaggle/input/ppg-dalia-processed\"\n",
    "filepaths = glob.glob(os.path.join(data_dir, \"*.npz\"))\n",
    "\n",
    "# 80%-10%-10%\n",
    "train_files, test_files = train_test_split(\n",
    "    filepaths,\n",
    "    test_size=0.2,\n",
    "    shuffle=True,\n",
    "    random_state=42,\n",
    ")\n",
    "val_files, test_files = train_test_split(\n",
    "    test_files,\n",
    "    test_size=0.5,\n",
    "    shuffle=True,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "files = {\n",
    "    \"train\": train_files,\n",
    "    \"val\": val_files,\n",
    "    \"test\": test_files,\n",
    "}\n",
    "ecgs = {\"train\": [], \"val\": [], \"test\": []}\n",
    "ppgs = {\"train\": [], \"val\": [], \"test\": []}\n",
    "\n",
    "labels = {\"train\": [], \"val\": [], \"test\": []}\n",
    "\n",
    "for split in [\"train\", \"val\", \"test\"]:\n",
    "    for file in files[split]:\n",
    "        data = np.load(file)\n",
    "        ecg = data[\"ecg\"]\n",
    "        ppg = data[\"ppg\"]\n",
    "        label = data[\"label\"]\n",
    "        ecg_resampled = mne.filter.resample(ecg.astype(float), down=700 / 64)\n",
    "\n",
    "        ecgs[split].append(ecg_resampled)\n",
    "        ppgs[split].append(ppg)\n",
    "        labels[split].append(label)\n",
    "\n",
    "    ecgs[split] = np.concatenate(ecgs[split], axis=0)\n",
    "    ppgs[split] = np.concatenate(ppgs[split], axis=0)\n",
    "    labels[split] = np.concatenate(labels[split], axis=0)\n",
    "\n",
    "mean, std = labels[\"train\"].mean(), labels[\"train\"].std()\n",
    "\n",
    "\n",
    "def normalize_labels(label):\n",
    "    return (label - mean) / std\n",
    "\n",
    "\n",
    "def unnormalize_labels(label):\n",
    "    return std * label + mean\n",
    "\n",
    "\n",
    "class PPG_DaLiA(Dataset):\n",
    "\n",
    "    def __init__(self, ecgs, ppgs, labels):\n",
    "        self.ecgs = (ecgs - np.min(ecgs)) / (np.max(ecgs) - np.min(ecgs))\n",
    "        self.ppgs = (ppgs - np.min(ppgs)) / (np.max(ppgs) - np.min(ppgs))\n",
    "        self.labels = normalize_labels(labels)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        ecg = torch.as_tensor(self.ecgs[index], dtype=torch.float32)\n",
    "        ppg = torch.as_tensor(self.ppgs[index], dtype=torch.float32)\n",
    "        label = torch.as_tensor(self.labels[index], dtype=torch.float32)\n",
    "\n",
    "        ecg = ecg.unsqueeze(0)\n",
    "        ppg = ppg.unsqueeze(0)\n",
    "        label = label.unsqueeze(0)\n",
    "\n",
    "        return ecg, ppg, label\n",
    "\n",
    "\n",
    "dataset = {\n",
    "    \"train\": PPG_DaLiA(ecgs[\"train\"], ppgs[\"train\"], labels[\"train\"]),\n",
    "    \"val\": PPG_DaLiA(ecgs[\"val\"], ppgs[\"val\"], labels[\"val\"]),\n",
    "    \"test\": PPG_DaLiA(ecgs[\"test\"], ppgs[\"test\"], labels[\"test\"]),\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d757858",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-09T22:29:23.564363Z",
     "iopub.status.busy": "2024-11-09T22:29:23.564083Z",
     "iopub.status.idle": "2024-11-09T23:03:58.833327Z",
     "shell.execute_reply": "2024-11-09T23:03:58.832372Z"
    },
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     "end_time": "2024-11-09T23:03:58.836307",
     "exception": false,
     "start_time": "2024-11-09T22:29:23.558695",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model params: 2.86 M\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c79ed04b8508444ca59baea95401965a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/32160 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=0, Loss=3.22\n",
      "Epoch=1, Loss=3.14\n",
      "Epoch=2, Loss=2.89\n",
      "Epoch=3, Loss=2.80\n",
      "Epoch=4, Loss=2.76\n",
      "Epoch=5, Loss=2.73\n",
      "Epoch=6, Loss=2.71\n",
      "Epoch=7, Loss=2.70\n",
      "Epoch=8, Loss=2.68\n",
      "Epoch=9, Loss=2.67\n",
      "Epoch=10, Loss=2.66\n",
      "Epoch=11, Loss=2.64\n",
      "Epoch=12, Loss=2.63\n",
      "Epoch=13, Loss=2.62\n",
      "Epoch=14, Loss=2.61\n",
      "Epoch=15, Loss=2.59\n",
      "Epoch=16, Loss=2.58\n",
      "Epoch=17, Loss=2.58\n",
      "Epoch=18, Loss=2.57\n",
      "Epoch=19, Loss=2.56\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchmetrics.aggregation import MeanMetric\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from convnext import ConvNeXt\n",
    "from loss import RnCLoss\n",
    "from ppg_dalia import dataset, unnormalize_labels\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "batch_size = 32\n",
    "train_loader = DataLoader(dataset[\"train\"], batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(dataset[\"val\"], batch_size=batch_size)\n",
    "test_loader = DataLoader(dataset[\"test\"], batch_size=batch_size)\n",
    "\n",
    "ppg_enc = ConvNeXt(depths=[3, 3, 9, 3], dims=[32, 64, 128, 256], num_classes=1)\n",
    "ppg_enc.to(device)\n",
    "\n",
    "model_size = 0\n",
    "for param in ppg_enc.parameters():\n",
    "    model_size += param.data.nelement()\n",
    "print(\"Model params: %.2f M\" % (model_size / 1024 / 1024))\n",
    "\n",
    "# Pretraining: Rank-N-Constrast\n",
    "epochs = 20\n",
    "lr = 3e-4\n",
    "optimizer = torch.optim.Adam(ppg_enc.parameters(), lr=lr)\n",
    "criterion = RnCLoss(temperature=2, label_diff=\"l1\", feature_sim=\"l2\")\n",
    "\n",
    "avg_loss = MeanMetric()\n",
    "train_losses = []\n",
    "\n",
    "pbar = tqdm(total=epochs*len(train_loader))\n",
    "crop_size = 256\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for _, ppgs, labels in train_loader:\n",
    "        ppgs = ppgs.to(device)\n",
    "        labels = labels.to(device)\n",
    "        n = ppgs.size(2)\n",
    "\n",
    "        start_idx = random.randint(0, n-crop_size)\n",
    "        end_idx = start_idx + crop_size\n",
    "\n",
    "        ppgs_aug = ppgs[..., start_idx:end_idx]\n",
    "\n",
    "        ppg_feats = ppg_enc.forward_features(ppgs)\n",
    "        ppg_aug_feats = ppg_enc.forward_features(ppgs_aug)\n",
    "        feats = torch.stack([ppg_aug_feats, ppg_feats], dim=1)\n",
    "        loss = criterion(feats, labels)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        train_losses.append(loss.item())\n",
    "        avg_loss.update(loss.item())\n",
    "        pbar.update()\n",
    "\n",
    "    print(f\"Epoch={epoch}, Loss={avg_loss.compute():.2f}\")\n",
    "    avg_loss.reset()\n",
    "\n",
    "pbar.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36aca8af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-09T23:03:58.852473Z",
     "iopub.status.busy": "2024-11-09T23:03:58.851911Z",
     "iopub.status.idle": "2024-11-09T23:03:59.319490Z",
     "shell.execute_reply": "2024-11-09T23:03:59.318564Z"
    },
    "papermill": {
     "duration": 0.477688,
     "end_time": "2024-11-09T23:03:59.321714",
     "exception": false,
     "start_time": "2024-11-09T23:03:58.844026",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_losses)\n",
    "plt.xlabel(\"Step\")\n",
    "plt.title(\"Train/Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2721f348",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-09T23:03:59.336450Z",
     "iopub.status.busy": "2024-11-09T23:03:59.336134Z",
     "iopub.status.idle": "2024-11-09T23:04:42.206839Z",
     "shell.execute_reply": "2024-11-09T23:04:42.205922Z"
    },
    "papermill": {
     "duration": 42.888881,
     "end_time": "2024-11-09T23:04:42.217448",
     "exception": false,
     "start_time": "2024-11-09T23:03:59.328567",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "09884874058c46c6848c394d696d0e60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=0, MAE=7.49\n",
      "Epoch=1, MAE=7.84\n",
      "Epoch=2, MAE=8.06\n"
     ]
    }
   ],
   "source": [
    "# Fine-Tuning\n",
    "\n",
    "model = ppg_enc\n",
    "epochs = 3\n",
    "lr = 3e-4\n",
    "optimizer = torch.optim.Adam(model.head.parameters(), lr=lr)\n",
    "\n",
    "train_losses = []\n",
    "val_mae = []\n",
    "\n",
    "for epoch in tqdm(range(epochs)):\n",
    "    for _, inputs, labels in train_loader:\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "        with torch.no_grad():\n",
    "            feats = model.forward_features(inputs)\n",
    "        preds = model.head(feats)\n",
    "        loss = F.mse_loss(preds, labels)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        train_losses.append(loss.item())\n",
    "\n",
    "    batched_errors = []\n",
    "    for _, inputs, labels in val_loader:\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            preds = model(inputs)\n",
    "\n",
    "        errors = F.l1_loss(\n",
    "            unnormalize_labels(preds),\n",
    "            unnormalize_labels(labels),\n",
    "            reduction=\"none\",\n",
    "        )\n",
    "        batched_errors.append(errors.cpu().numpy())\n",
    "\n",
    "    batched_errors = np.concatenate(batched_errors, axis=0)\n",
    "    mae = np.mean(batched_errors)\n",
    "\n",
    "    val_mae.append(mae)\n",
    "    print(f\"Epoch={epoch}, MAE={mae:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ed824d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-09T23:04:42.232849Z",
     "iopub.status.busy": "2024-11-09T23:04:42.232542Z",
     "iopub.status.idle": "2024-11-09T23:04:42.493060Z",
     "shell.execute_reply": "2024-11-09T23:04:42.492133Z"
    },
    "papermill": {
     "duration": 0.270391,
     "end_time": "2024-11-09T23:04:42.494921",
     "exception": false,
     "start_time": "2024-11-09T23:04:42.224530",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_losses)\n",
    "plt.xlabel(\"Step\")\n",
    "plt.title(\"Train/Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e1bd8a6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-09T23:04:42.512644Z",
     "iopub.status.busy": "2024-11-09T23:04:42.512330Z",
     "iopub.status.idle": "2024-11-09T23:04:42.768683Z",
     "shell.execute_reply": "2024-11-09T23:04:42.767779Z"
    },
    "papermill": {
     "duration": 0.266974,
     "end_time": "2024-11-09T23:04:42.770612",
     "exception": false,
     "start_time": "2024-11-09T23:04:42.503638",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(val_mae)\n",
    "plt.xlabel(\"Step\")\n",
    "plt.title(\"Val/MAE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8e146f75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-09T23:04:42.787991Z",
     "iopub.status.busy": "2024-11-09T23:04:42.787656Z",
     "iopub.status.idle": "2024-11-09T23:04:44.966214Z",
     "shell.execute_reply": "2024-11-09T23:04:44.965220Z"
    },
    "papermill": {
     "duration": 2.189623,
     "end_time": "2024-11-09T23:04:44.968324",
     "exception": false,
     "start_time": "2024-11-09T23:04:42.778701",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE: 8.656865\n"
     ]
    }
   ],
   "source": [
    "batched_errors = []\n",
    "for _, inputs, labels in test_loader:\n",
    "    inputs = inputs.to(device)\n",
    "    labels = labels.to(device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        preds = model(inputs)\n",
    "\n",
    "    errors = F.l1_loss(\n",
    "        unnormalize_labels(preds), unnormalize_labels(labels), reduction='none',\n",
    "    )\n",
    "    batched_errors.append(errors.cpu().numpy())\n",
    "\n",
    "batched_errors = np.concatenate(batched_errors, axis=0)\n",
    "mae = np.mean(batched_errors)\n",
    "\n",
    "print(\"Test MAE:\", mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c03485e",
   "metadata": {
    "papermill": {
     "duration": 0.007876,
     "end_time": "2024-11-09T23:04:44.984506",
     "exception": false,
     "start_time": "2024-11-09T23:04:44.976630",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Examining Feature Space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e36f64d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-09T23:04:45.002938Z",
     "iopub.status.busy": "2024-11-09T23:04:45.002468Z",
     "iopub.status.idle": "2024-11-09T23:04:45.975291Z",
     "shell.execute_reply": "2024-11-09T23:04:45.974503Z"
    },
    "papermill": {
     "duration": 0.985114,
     "end_time": "2024-11-09T23:04:45.977567",
     "exception": false,
     "start_time": "2024-11-09T23:04:44.992453",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "batched_feats = []\n",
    "batched_labels = []\n",
    "for _, inputs, labels in val_loader:\n",
    "    inputs = inputs.to(device)\n",
    "    labels = labels.to(device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        feats = model.forward_features(inputs)\n",
    "    \n",
    "    batched_feats.append(feats.cpu())\n",
    "    batched_labels.append(labels.cpu())\n",
    "    \n",
    "feats = torch.cat(batched_feats, dim=0)\n",
    "labels = torch.cat(batched_labels, dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24ccef7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-09T23:04:45.995539Z",
     "iopub.status.busy": "2024-11-09T23:04:45.995246Z",
     "iopub.status.idle": "2024-11-09T23:05:39.214758Z",
     "shell.execute_reply": "2024-11-09T23:05:39.213915Z"
    },
    "papermill": {
     "duration": 53.231053,
     "end_time": "2024-11-09T23:05:39.217131",
     "exception": false,
     "start_time": "2024-11-09T23:04:45.986078",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import umap\n",
    "\n",
    "feats_reduced = umap.UMAP().fit_transform(feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58b3f34b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-09T23:05:39.236157Z",
     "iopub.status.busy": "2024-11-09T23:05:39.235088Z",
     "iopub.status.idle": "2024-11-09T23:05:39.579434Z",
     "shell.execute_reply": "2024-11-09T23:05:39.578528Z"
    },
    "papermill": {
     "duration": 0.355794,
     "end_time": "2024-11-09T23:05:39.581439",
     "exception": false,
     "start_time": "2024-11-09T23:05:39.225645",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HmbOQIcOjgU3fvaepxJi2heqWQvZQ1AyU404j2234xwIYDTr3D3RiiHEpjTR03m7sVNe/7aim3IclnMuBrTP88tfexN/96lXs3zrXO1YMobGsLio83UlvUsDqAuypL+FAQzGYN5EeR4ZsZY3wk+8+xJ88/e8QgYWo170SBDWIm9HmKIBzHCzwP5+4pfna9z5/B//+jv/mHV/835x0btZZ81iGshZ1iJGFdP8NGzZ0vP6ud72LP/3TPz2ksfth9+7d5HI5JicnO15fvXo1u3fvXvI4mbOQIUMfiBV23vowlX1lRlaOsvYpG9GeZtP1D9Jdq9DRIyEtayQlKrbe7E4bjAb992/OgSTCoBzXoX/dQtv2TaunaDWUHo6v/uU32drHUUgRC8yFsCwvpA6STsoeA89FRrqbVC0GrRRKpOkJyABXIHXGTKpFQev6dEdprHWGvx+suDl3Q/o4CpH0XjcB5mdrvOMF/8rfXP9m1py0fNFzzPAERTsD+VDGALZt29bRdfLRiCocTmTOQoYMXdh0zb1c976rKe9qGdHRNeOc/uJz2Xbjw0Cr/K6vmFLiMJQ8WDAtQ9husEp+/+ZMg5AqOS4VnlqaXPOPv7tp0W1qRlipWpEFldR6RtY5DAfrLLjUQqIBITS5Eb3RE/dKZN35tF8/Xzt+SOo0DHWi6H/tuteHhuERmXoj5vee9iE2nrWGt/zLL7LulBVDts7whMRhJDiOj48/Ji2q16xZQxiGzM7OdkQX9uzZw5o1a5Y8TlY6mSFDGzZdcy//9Tuf7XAUAMq757nlX66nvT/TYoY+0J0rZq1U0+Cl6QZwxnAhco9K7IiD3cZtqU5Farc9NdyANrdfggdixG3XvWU5du/5eunzUwgjPvhKkVMuqmAT1UmV6EC4a+SOVktqLz2lHD8kebixkigHLqrQL3qQIu4Sw+qWlHYVGku5FsK2u3bz+8/4B+6/JWualeHIxwUXXEAQBFxzzTXN1+677z62bt3KxRdfvORxsshChmMalX0L3HXVjdz/1Tuoz9YwkRlKvFPOMqXPFh0/ry0Nq2kpKLpqBZJhZkNn2J0GgBNPci2rhaLn3kvRT7GxHYLrG2FEsMqlOqIkfeEptzIw4lb07hSEEV8TWkufVH/rnHHRhJQ7oEWa0QUP13eiodyCqVWu2ekojU6VeP7vPIP5O7ey/Zp7uHfOhWYONAxaQcnT5LRqykvH1vElUCpRe+xHsnRpEVGJuJO4cxx0KkYsWqWfxSODTa+FEd57xSf5yL3/H57vLbpfhicIHofSyXK5zIMPPth8vnnzZm6//XampqbYuHEj09PTbN26lZ07dwLOEQAXUVizZg0TExO8/vWv521vextTU1OMj4/zlre8hYsvvnjJ5EbIIgsZjmHMbjnAF155JXd87AdU95WxkWMHeoqBCozg3otFLZoWMOJIjCO+JEbfraBLvuMgzDRamgpAx6pZxIX3u3PzmlbUoZ3BbwESLoGv066TioIHYx4UtONRRLZFmBQUgYbVRY+J3OATHg16iZppyahFNcWOVFKJEWhpphFSwuC5L3wyz37dRWw8e21SZqmQNv5BObbMR5ZyZKnGQsO4yoxAK3w9+DbVHe9IIw0d2yQXzDXDsoetj09jvsGt37z/8AyW4ehAU370EB8HgVtuuYXzzz+f888/H4C3ve1tnH/++bzzne8E4Ktf/Srnn38+v/ALvwDAFVdcwfnnn8+VV17ZHONv//ZveeELX8grXvEKLr30UtasWcN//ud/HtQ8lBxhLd7m5+eZmJhgbm7uMcnnZDg2ISJ88VX/zPSDe5E+y/W0FLDfSl4E5mMYHeBRSELaa1hFMVl0Cq7MMEXDumqD4hCvRJLW0/lkDCtu5W4EAjWc8JDqIEDamloR2eGkx5mGpdJFdFDA2pIm6IrxuxSAe81XTmRpNnSExzQ6kF6HegxeweftX3odvjH88yv/jX0Nd1G2V6KBc9IKJnPBkBn3OgvuuEm5pQixCMa6aIuvFQXPS/QuemmVobWLxhxyWuM3ORbCM37lAi595TmUxgtsfPLqvhGQDI8uHgubkR5j9vt/zfho8dDGKteY/Nm3H3U2LktDZDgmsffOHRy4b7DcadqvwQrUjKKelCRqIO9J8rpQSIxoSmqEVEVRUfTb7Lkk7aptavSlmXoYZGCUUkQiKNumjiiQ09KVge8/fyFNafSWCHZDRBgLVIez4CtYWeh1FNJzhFaKwkteMZaOTpUqiV6YhuGvf+FfufTXLnQCS8kGo4HuW+7o5szQ6+OO31v3kaZMSr6mYYS6WCKBfBKhUCpJB7U5TwrHo4iGrJ0UNDkraSXFdz95K9/95K0ArDlpiiv++DKe9qIzB46R4SjHMdxIKktDZDgmse/uHYuy8ozAdKioGrBJv0gDVI0iFkVkFRXjpJZjcds3rCP+qa5UhlJpiNwZIystQzsU0tbDAEAJ+SX8aiWZS9swQ6GUSyesyGsmc4rVBc26kibnDZ9f99uRON5E+zzSUkuA6z52M+UY0jLMicDrGaMdobU9ZMTWOcnAAlEhbaSlKPoexW6dCKXIe7rD+GulmlGDbiics9H+ebWXaooIux46wN++7rN85UPXDz6hDEc3XAe0Q3wcUcH8JSNzFjIck1DecKKbJI6C26LdgKjmcyvp6t3xC2pJwyaFI/312B1xhj6nhfTow7KAkmoRtM0zp/trBnRjKWWT/TAaKCZymoKvFlnRt5yAbhnrYJH5xaKaTpOnFasLft8bka8UcbL671e9cDAYdCqBpykk5EqfVvRAK4VO/s4pRUHrVolncmxRKSm1cy5X/fm3+KOfv5Lq/CErVmXIcMQgcxYyHJM47uknD11uh9YZtcEVD6rZCrrPW/0XD8qlMEZ8Ie8pIrt4ZEF1ZdbTqgY9RKdR+nColpJJlySKsZRigZSYmRrKWuyOkdMpR6KF9qftwlGtySlyniavFYFW5LQir12kQylF3VjiNqPccqIGo/ttrYY7P1prPM9JW2vt/vW0JtC6SbBsHh+IGe6wPHT7Tt78lP/Hrof2D59ohqMLqSjToT6OQmSchQzHBOqzVe79wi088NU7qM9V8SdHIR8g9ajvqjNMCQJDrZJiEHugZjq1FNLtdSJIVPKESuyqE/y+pYHSd14ioLSLXFRM7xzT+1CUlkc0jzwc6X42SXl4DF6N+yoViXI7LUQkHIF05jRlsLUCtcR7o+M+9EdkhbwPdSvN1f9ikY92aKSt9XcfDob015F0FRTSXFUNUolMx23nWFTm6rztZ/+B//uJX+aC5542cL8MRxGOYc5C5ixkeMJjfts0X/+1j1I7UEGsEBqY21NHAaO+C5unpX8Ho6o4CCkZMe7I3zuD4ydWZzInRKKYboDVruqhZcScQdTQM5nQuvJET8GIcg2jjLTMnFJCPVbJCO2vK3ykJz0xyPZZcU6BNLeTpLrBhTZMUm1Ri10lxHjSY6ndELf3u7AJQbSdCJpiKWkVgJKv8AyEMnxVr9r+n56j1jpxCHpJpelYVlxaRNPbHTR9ml7V7uM3haI6zl9hjeWvf+Uq3v3113Hazxy/tBPNkOEIRJaGyPCEhojw7d/7HLXpsmsCI8J8nJa+KRZitzIOrVuNh0lJY7f64oDR+9T0twxs1IxOuO0KurW61QpWF4TjSrbJgtAIXiJ05Km0msANaqxQjZ2xD407jqdcx8pRH4qe21cBlViYrlvKUdPUJ8dUBKq16h8WDU3fKnmQU4IRp6lgBerJNavGQsMKRR/ynqLgqb6cCpGWkwStDpsptHL7DoOfXI9wSHQA0moFl7yxbde64MFUXjEWdM6l/Xw93brmCsGKbXIStIJnvPJsN48+xx3kvKTRhne96KPs2z479BwzHAVIc3yH8sjSEBkyHHnY/eOtLNy/k2KyVJ+PdZeJV0QCUUd/A2FlDipKaIjbphft0sTJ71+lrkELGqdDkBo7cbs2V91jgdNMmI6GhNST0sSZhrvXjOdgbQHySX+JekzLARJnGAuecxoWQkspUB3CTzoRSmrOse0c2ueePm+YlCfgLKWnXPvs0MCKgqbot1brkugZxLa1OlfJ9un1yGn3qMat440EmsiagQqVKwpejwbEIBQ9d0457SIF7X7FuA+rCo6UGifE9D311idsRShHJlG9VM0UkRW44T/vSu7zvfNw2zinLnUzPa2aXAlrhT/+hY/w4ZvfSpDLbrtHLY7hNEQWWcjwhISIMPvwAW7686+41XRiqFur/WFQGIGVBdtW/ti5Sle0DCBtm7SzBEqeUPSkh7vgwtit5/Um92DIjJTTKxBcF8idNffvQuQcoHQ93N4/YTTQeBpmGsJswzIfWmYaloXIiRLFif3XOAcg8BKeResUsJKQ+drknlP/qRQ4pySdX/u/nuqNIDQFppSTtx4LnGHPJU7XZN6j5OuOm1LBU0zlPWyfFEo3Sp5iIudUK/NJNYpNRJkkSQnllZt8Xis8pXjKqy/k9X/7UlYdvwyloRI7RyFIxJc60hVJqML5Talz5iIPobE0EiJmKgbVMJaGMUTGYETYv32Wj7/j6uEnkeHIxiGXTR4GZ+NxQubiZnhCQUS47yu3c8u/fZ/ylv1MBO6HaQ4y+qeTFfTqvGUudnLGqaORtopudwDSv9MQv0IoeYsf0NpUJXJwZEGk1dkxNc414yoQdKoeNWD/UV9Ri50xs0AxicG7iKhLLygcOTEdwU96SqQkx35noYCRIeWVKUciaj7v1WRQSpHzIOdBHLrjjASKkUB38AqstcyHrXH6oZhEUwbNR7TrgSHK8Sds4POCP34Oz3rdRQCsOH6Sv7riE5h66jwNj/S0I2201fe4pJ+ve/+/P3ojv/h7z2Ry1djA8TNkOBKROQsZnhAQER780m3c/uFraExXABjxhNgKWmnH3ifJVw9tqeyqFVLOQOBBCWFtwbCvrpmNNLEMNs4izthM5cxQtv6BBuyqwVzkDP7xI/2VCkWcjHIkbl5F3/ECIDVSi1QFKIWnDGUrLMv3/7kLQjVWjPgtAqKvQOvUweod39fDj5seWytpClANQ14LkQGViDXppCe3oJqNr1K/qN9hi97w0sjCSI4XveO5lA9UGV81ytnPPY3CaB6A3Zun+YtX/weVivNIPN3prAw7P5tEEYZv2PrTWuEDb/gs7/nqG4bvk+HIxCPo7dB3jKMQmbOQ4aiHiHDjX3yNB794a2c1Q8KCb4dWrjVyONDgK4qe7TBIJS1UYkXF6EWjAEqBh9NRGITtFdhabUUErMB8BONBr4GqxE7oyVcubO+OkYT6k7yHa/M8yHkR5kLpWa13n3O6Ak65FSnRr3GIEVOF44MILi3h6bQZVmsujucAEkvTCXEfj0qULxWhkcQx6q1YyS3BcanN15lcM8ZFv3he87V922b5yXUP8e1P3kpUDskpRUNkacqaCYZWZQwY4q7vb2HnQ/tZd/KKJR0jwxEEse5xqGMchcichQxHPKoHKtz7pdvY9v2HsFZY+5SNnPmLFzC2bhKAnd9/kAe/6PT5O1IDbX0bWhBGfAVxyqzvvNmXPKHQ5l+IQD4RChj1LA2j6V+R3xpf9zFoKcpR6ijQMcaBhkt9jAUtA9RKPyhGuxyFjr+lVSjZDSuuYmGZN5yeJCLEojpuCCOeq3ioxa7BUj4RLQLXA2LQ+bfGhIXQulSHcoTCIKmY8NJ8Da1QfbuD1fE5AhN5xUzdRSnS63uwaCSRg1q5wT+/9cv86Gs/bU5X4RZ86VVaSmShOdGeMsrFd/vsB77Lb//DK/AW+VwyZDhSkDkLGY5o7LhpM9/4P1ehwoiC5wiHW36ymc2f+B4nv/g8LvyTl3L/525Ceaqne2R3CZ9b9YNRwmjguhKGtiUe5Mr+XOjcwYX+VUL6G/WFhrUJobATVtLWz4oIiBuKcd9S7PqF7aq5cfs5G/vqMBsKY37CG0haOec0i652tertkCniyi2XCmmPtoiwoxbxwELUbK6kgKKnKSgPTyuqscfKgsbTLensyAqzDaFhnUMw6kNohFBcXw1rJKkQcIeJjLAQCbWkMsJTUPA7+QcqMcgF3/E0Wn013D6xAYY3pwRg1UnLscbyvl/+FA/csq3Hu7K0Sh2XGllocUaWDhG49rN3sOmOXfz5l1/H5KrRg9o/w+OILA2RIcORh+r+Ml//9U+RszE5zxELI1HNhdx9X76dzd+4g0g0Yhbrw+iQ94SqcTd4T7kW0jZZwc8l5YtFD0Z9i1adzaAEsKLbniWrbGk31CrpFyHsCz3GrWUy14oUpE2UBiGyjseQ107zoeD31wVoRyvC0P6qe5J2mzRWkl4Ow44tFJO57ayFbK9FHe8LUDWWGpYcHguRZVcV1pZyBFoRWmE27LwRzoZQ0DASgBFXZdKIBT9QNIywv965vRGoRE44azzXOd+858ia6VzS70EoQmRlgBKmc4I2nreeNaeu4tb/uY/7btzadxvnrLhyx/ZogUoezUqQ1k7JXFJnSjVfXoqvsfXevfzJSz/K33//LUt2TjI8zshKJzNkOPLww7/5Jjkb42nHSbRtjoIri1PUQzCR6dspoV+o2lNOHCnZgtAK+xqKhVjRsC5HPxspdtQ8YqtQbVrFC7HGKsjr1HzQ6qfgZtV2JPf3fKypx92v9kJEWJ4XTh+HMycVp4wrThhxc3wkIXcjMBe5sk0N1AbnDZqGcj4RRqjHpsdR6NgeMGkkQWBHNWQ+tD2OQoq6hXLU6u0QJ5UYB+qDTyyyLoqQIjWmJilNNCLY5AEwE9q+yorpub34Hc8F4PrP34HuwydJHYXmO8nfgYIg6VMR6ETUSgRrXUqkYQytb1NnCmYp2HbvXu743kNL2zhDhscRmbOQ4YjFtm/eSc6zeEqaYef0QcdzF4+24jQL5iPFXOQcgH5OfKBhRDvFw5lQtykntB4C7KrrpFzSjV01iti6BlLNRkrNUQdzGOZi1zTKiFMRbEdsLfNhzPoSrCuqDm2Coq9YkV9CwylxrlJOC4F2/85HiSKlOCPXiIXItnQBWru6v+cbxh0HmIniRaM0cZuskxGoxMNXS04EqRUVKEeDmku3UI87m0dFXeHb1EGMcee6r26pGzr2qRkhXjbKiRdsAGBuXxnbR/lJ6Gw2peiMKrUj8Fx1jUUIlNNj6E4sHUyg4Kv/+P2lb5zh8cWhqjcejjTG44QsDZHhiIMJY276k88zrmKUhnR13bCKSPr7twaYaWgiac8hKxYiYSpnyXs0VRaVuLLActSvBTXNfQVhNtKszNsk1aBaI6tWaHp4YaAitK2yv5UF2Fl1+fHQWhYiw9qiZnm+t/RPKRjznSGMbcKf6GeFlOtnoBLSYDV2ZEIRYabuTlopKEeWvKfIe04REqBhhEpksLiWzAUP6sYuKQvfntuPrE24C4NRNaCMJadV0t67ixjYdR0trWSPUoraAIck/RxigenQyWdrlSjrKvjV33x6c54rNkyib9qG7Yq0dFdotCtedhwr4TQEWmGMO7iPQsRiEHIsUplLLydyz9bZRfbIcMTAymFIQxydzkIWWchwRGH6vl184/L3s+s7d/eIHtkOR6AFEZhuaJqtEJoRAmdw9oeahnF/W3E389BAtal8OAiKcqxd+Z89WBpb+yitltI5DadPuOflRGN6fWnwz1ApmMrRjJy4820Fvh0FUhIDJCxETtnRKRR2tocG5xykSo4zDUslstjkGowEiqKGnO4224Pm1tpqKdfGST0rYhFs4iq0P9xr/UdKhB/7z6PruQA2We0/5edP5Xmv/xmssczunONnLj+9x1GQtnRGOt6wltapQmZLJlvwVev71j4haUuX2CSq052iGJ0sDjizDBmOHGSRhQxHDGbu3801r/0XfNO/bbRD7xt1C/EAnYF03VkxmokufeYlrZ6Tfxs2Zfx3HsdXnV0f+42Q1+L6Q3jOSSh5jsS3r+G2GKaECM5hGPFhT8M1uHLy1dJMhaQr1dAqfA0TSQfIhcG0g7axW8vcoq/YXBEQDyEeup/Xdb7DFA9TlHxF0dfMNszA1XeaqFCo5PxUUyRr+Hxcqaxox2s57rSVPP8NF3HJK87h2n/5Idd/7EbK+yuICMsm88zMuosvItSNaREbDwYJ9yKNwygcl8NDJa/3b2nd3V67Xq0f3HEzPH7IqiEyZHj88eP/dzXEkTPJfe7bWklfUaS6SSMObSvdNiIkKOpGmGgrr1PKqQbGpne8FoS8civBQYFHX0FidvqM4yaxImebqotpOqJhW9oIRoa3aW4/fmRpSihrYDJxDMI22ej02i25rE9gRVFTS2StJ3MeM5GmOoQU6XUFJfOeXvRIgktXLBamT6/keC4pG1XO6EZDor8KKAaa055+Ak95wRmsOn4Zp118Ap/4zc9z77UPNqMxSilKUUQcKCoWwsjJYIvIUqovO2AQTNtZpx+hkfQ70/+KGARPUodB2PyTPdz2nft5ys+depAzyPDY4zCIMg28mxzZyJyFDEcEKrvn2HPTJlf+NiAqn9NCZHqtqktPqGaXxI6fojij2s8Wj/ku4jAYTs1xR03je2l4ujmsWyAoV2pZazaDSo/UchSKPj0pFV+3zPi+umVNUQ9c2SrlOjT2nDc0dRSc4encv7jYcpwkTI4jE1qcSqRWihNHizxcqVOOO027AgK8jrmOBz457bgZFvchdEdKUjnm2qC2kl1QCKWE7SkiLITDCZG+hii23HXdJu66fhMIjEwW8CoNcl3XQSXnWbRCeXKEfXsWEByHJO95ScpgsLZFWmHRLfOc8hkQWdQcGAQ/SZcpJXzgDZ/nqk1/vNhlyfB44xiOLGSchQxHBMrb9jPqxR31893QCnIqZbq3XveVNI1e703avb6iEHLa5CwnjJXJe84AFjxhMrDN7dr3ARj3LXkNDeM6FvhJLj99+Mr9gLRyyo857ebiKddEak3eMJWTvlGS9cXWi9sqJjmnfnwMt6KuDliO76kJB+q2f5mobvWRGIT0vrUQuUoKrZIqCgOrcnk2FgusyOVYHgRM+AFF1XIU8loxmfPJe9o1hdIQtPMYxKVKSr67doPOsR/Gcq0y2cgysCwTkhRF+0VONi3P1pgLLdvKEVvLEfvrhkZsqceOBBpaxdy+cnO3OElJWFrlsP1KMZVSNAZEXZRSS4nldI2tqMzWOLB7fol7Zsjw2CNzFjI87oirdXb8/ZcpeobI6qFlZzkteF0uwYhvE5pfP7jB9tTz3DszjhW4cNV+jhutAIoJ37IqbxKJZ0e1y2tYmTNMBoIoWFHo50rQTJdoXE+DgjasLVjWFy0r8pKoEPY/j9FAsbHk/q4auHMmTjpjduoHRKLYUx+cTLAiNIaERZfldSvW0STYuYdpWwELroxzNhQqsVA3QiQgaEZ9n2W5gJX5gKl8juU5n5WFHOO5gKCtAkIpRdFTTOQ8LIa5OKTgqQ4+w1LFh4qeq26oxK5kdVivDW9ADielaQba9fRYiCw7a6ajzNPvcm5ia6nGMfNRRCWOe5xPAWqxaapa9j/wQXIfElz/hTsf0X4ZHkNkpZMZMjy2iOsh267+Cftu24y5/2Ea2/ZyIMwRiyv7S4l77UjTDHlPKCq3mo4trC2GzIWaqh3m+7oyyHy1SC32OWNqjnqs2V0tUvKgqG3zGE0p4oQDoBXN3gH9fuYtjoAz/KUhTkI7nrpcY8SyowazoeUH+yJWFTTjgWJlHmYizYJpCT9FSS1hmsZQwHQYkvcUSuX6HqPgOe2GSizN82imULpQM5D3el9PORWecmOFfXyTNNKSOgMTvs9CHPcwOTzV4lwMgqegHKu2aygUfE2gXVSg6MN44KIVC5Ekqpj9oZQjSirT+uzmI8uUdk5MyXfcjDS10I6GFaIwxlcKLyn3hMFchPZr8Uhwz41beOmbL3mEe2d4THAMKzhmzkKGxxwH7trG93/nk4RzNbQG38kcNTnlVeNR8CwBnSF8I1AzHmN+RDX2CK0mry0FXxYlzYFKOigq5sIcB+o5ThivsK+eJ7TaGRT3ttNUSPZJMchRoLmdYEVzoCGcMJKQElWn89ENXyuevtLjQEPYWXNqkqO+cMKoq5jYWm0pQHbzMEwiPlS3lpp1+fagT7mfUq71dCVefEHTvmdBw9oSTVLo/gbMhI4AUjdCkAoRqVZapvvYI57fYzi1UgRK2spc+8/DSpvWAQCWWmzYMOpR8nUzhF9f/IMHYNT3CK3r7QFQjy0jgUdOKwoKqgPmY3GkWmO6+0UM7h+xVGehe/8dDx5Y4p4ZMjz2yJyFDI8pavsXuO7/fBxTcx0AESd2FHgulJ+2RLaiUNo2DYYRiMUte4ueU2msGE0aUfbUIpYQaas4EPZWCzx5+TyjvmE69AYar1YlwmImwFnNE0YsCtcsyc3Z/cj62ZU0kr08r1hR6NzACCzPw9bKcO70slzAdBixudzg1LFCTxMkkYQouIhVbTf2qwpw2nj6mnt/xBfuKVex1mOZnxsY+m9HIeEydGPEcz0yYmjXz0IkVcV01SvKCMvzLuoyFwmnjHn4CnZUYvY3LLF1xMmct5hehns7pxVaaWrGElphJL1GbfJa/WAQip5zLFISo4v02L6fjVIKJUKcpIc0vU5cd+kpCH6uT1gnw5GFY5jgmDkLGR4z7PvxFm5595cw1bD5mhUIkvLEQFlAUfIsRV86VuUKGAsSSWLVMqAN0ZQjzfKcoVobbjRGvHQvRcO4G7OvHCkxlvQo7XCU+LBPBcYgWHG9BJQCD6FuFAXP9RhIz6f5L50GuR3pij1MSy76QCnFmO8zE0bMR4Z752usL+YYz/nJXISZMMZDmMprphtDSIK61SK76CnuS7h2Iz4szwv3LjTY04iBmKoxbCyMLMo/6CY7qiSqUI2dZDYizeuRXOqOtIXC9beYi4Si59pb/3Q2or2RZtW4/heLpX1U4swFSjWjCzZJY4UphyMRhVIovDYDP+p7rm9EKgWtnAKm52ka1nZ0+xQRqhJSlYj2BlMFCcjjJ/v219U49YLjhp9EhscfmbOQIcOji+3fvosf/dHn+v9QVEoUhDHfkC6wussNwfV1sEIi6+ywpxFwXDFkZy1I6t57jb5zNlrUxEALsVWEVjHiWyqxToxQ5/y85iuDjXY7mhUZifPhKZcC0EqR106y2YjLyfsKir6QGzDsTINFj6mVouC51XI5tty3UMdXTpwpsm4FfM5kkZzWNIyh0tWyWpE6CopRX5H3NBXTOteFWJiLFHfPN5r7FD33AS3WylmhmukCY6FuYqd8SKqAmBpfmuO161t52pESAcZzmk3znY5CipqxlHxvSa2lRYSc0onmQyJmJYZGn0RWIJoxP+hwFJrnlkQY8rqlRyEizNkaYddYglAjxGIZlTyqJyrjTuo5r37K0LlnyPB4InMWMjzqiMp1bnrXF139ubTMbksut2WiA51GFPrf9EUgxrVD9pUl0M4RmI98zpqocfd8sav+3fU8WFMwHY2BVhbrTNfzTcb8qG8pxyrRbACUU0rUSeRhOmpf7/aHQjrY9QBKCTmtqFuIYkXNuCZXrYbajqewrmh7yxwfIVMullRsCsYDTcFzUtgri5piZJmPWjoCOiEA+hpGml2s2g+saFjTN00z+DNyRjs2naTBuhVGBqQm0vEU7jMONOR0q4QxMjKwfDS0gsSGoqf7XrJu8WqlWk2iFkxEQ/oPHGGJxVJQ/dMDqcPgKydh3SDucRTa0SCmoANyTZYHpN98q4TvffFOTrtg48D9MxwByAiOGTI8enj4v+4gasRERjX7EIAzrjktGHE3eY2QX6SSIA1bbygax+5PtrUCNetz+kSI3riGLffNYI2lqC0jfidfoegZSkHE5tkJasYZSk8lok/iDEku0RwQAeVByRcOhJrqkJREQffTVFDN/1dixVzc6xGUY9hU9jh51HR0nRxfwq9TRIiNa2w0Gbhwed1a5kJDTitOGc03eSAKxVjOYzRwEQcrkqz4FUV/8UhBioqJWTUoHELLiHb7F/1W6P3gIi6tqATQExHpRmSFyBpGPY3vuYs4qMOFSt6zIsxGYd9tUpRN5IiaQ+atk/xJzS6ur12zITldbIumQCyusufuH25ZdP8MjzMS0a1DHuMoROYsZHjUsetHD9IwaW8FB4UkeXzVzN/HoiiomGFL6vR31i1lrBUUPaFmFRvOP5GLfv8X+O6vfyxxTlosurEgYjIXsXluAouiahT1SDOeqCx6Sih4rWY/Tc6EghV5y4GGq/tvkeLcv0UtfcsO032t0MdRaF2NWIR9Dc26YmvVMRIk5zTEQcl7ijMnCx0ellLOEIoVTJuUiiTXzwlMQS1qEUp9PThSUNCanFKEyUWpWUPNxBS0N1DhsLtBpKRRJWk5KihH/uvupuknU9Zak9OOjLhU5cdQBH9IZEopxXwYYcQRahdb41mgMSS6AK1vl1mCjK/BOidNOr+ZAF6QERwzHLnInIUMjypEhJ23bQNchcN0qDjQcH+XPFhXsowm38L2uvrFY/D9jUdRCw99+VbqlZCc71YBqZ3xlcWIz4GG3zxeOdbUjKKoHY8h73USK7uxLGcJItVsXBXoRD1xkRVzOYJabKkZZyh9pRgJFEEz5KGYCWFN3rXPFnE59fUleLhM3zRAoF3La0/rZIVKs7umVrimSgOuXGyc8JLroDh06iilWFMosLVWa762rVHhhMIoQeKMpNEE1VYF0jyeuLJQK0KjnbMirjQxFpdySDkMXpvjMhJowoZZpF6hhVEvKXvtEyURcaWTsTgi41IdkH7NoFIopTBmafUyyR6YpJ2116ZrrrXiqZdlvSGOeGQExwwZHh1M37OLxlyN0MJP53SySnbOwFwk7Kz7bCxZThyxKAXGKjzd32AvJXonAhLG3PGVO4mNj1aOjzCVs10lhS5CYEWYytmm4JFepJuQTngMqeKjr4TQqkSop7+5iAxsrjh9graZUo6F0UAxESQleShunYbzlzmDH4trTz0+6TpU7qkrYuvC9COBe7STBH3ATzUV2tIf3ZdNRJgNbQe/IraOL9JtYBuxZSEWiH3GdcB8EmqPRXiotsCkn2NZkKOoPCeoZazjQ3Q4D85IB0O8ktC67pye1h2KjwVPMxaIIzou8gVYkdfUjFCLY0q+11ONUbdOq8FdF8VShZl9WufSjjRaksYTCiqgIsPTGjl8JOHRpFBaEeR9nv+rFy5pPhkeR2TOQoYMjw7KO2cRgXvnddJsCVKjmpL8tlY1RU9YWxQaVjOuYyJUh23ovE8PXmfOR5qHa37HuxXjsbfhcXwpZtRPCGUIJc9y/rKW/F9oFVWz+E8iTSsoFDGOABnKYPXIe+YHyxyUI6EeS8KZgPkI7piFJ0+2VvyBB+tKsK4kLISKiu1v5lI+RzFxGNrz4u2oxkJ3RWZoXKQiNYpWhAM1S7PaUinG/SJ5G1AxIVo74ubqvJOB1sB8aNleNRS0ToSVhNC4lbzX5tQoWpGDlJdgxZEZV+agIUIjlqZyZNHTrkojclLN/c59PFAsy2n2L7jPsxwbNK71tEtxtaWsSKIvCath2K3bw1VvtM8V0lQP1NvIakUVdJRMdkOhmuWTgquS0EqRK/i869OvZWrN+JCZZDgiIIeB4HjIXSsfH2TOQoZHFfnJIguxI/cNhrC1qllTMKAUldgpOLaTIVv36faqhM6bcsPAlmpLCbLzCMLDVZ+TRyIUMBHYnrbQelFhJwdjnZMQKGdMjDhyZisLnRoXJ/88M3yx6VbkQlNhcm8dTjJQ6JPCzntC2fbjCSSGWLlyT61aC5juK7UQWbrrBixQi52jYa3lQF3oJ8uQ1z557ZQZTxz1OsSZCr6ibg11a9CoDjEiTauHQ/sKPY1ApPOdDqWnPLKeaClM5DzGA03VCPXY4GvHvch7momglRpqP6e+KYTEq1JKURCP2pAKhjHPhZqMdGpimPTithE5FYoJCsxT75GE1ihGKTTPl4SnY4C/+vqbOPX8TGMhw5GNzFnI8Khi1XkbKXt5XEeAQQ6DomacsSx4EFqneVD00uqC7v1aN+K07FKAfQ2vr6OQHkMQ9jZ8jitG7uavOrf0SI2+2z62Tg9BcGmHguccjFgElKYaO+NWTjQaFDDmq2YTKQvsrfXOZCnYXoFT+iw029ckRlxEIG5zCnLaRQjaiyDTuv6UXBga5xgV/M5oSCyOW+G0B4bPT4D5SFiWd2NbaylHlqJ2Ho6nYNTXaKBmhUgGV0K0V08MKnqILFQjy0hOsy4PY0EueV2oxCSh/SWGd6X1LQk8Hy2auo0TjQ4HD0VOecRWCDyaTkGqHaESgm2q1AlgsSilGZciMYa0DZWPh0+rZNRFFtz3TGv4/tfvzpyFowVZGiJDhkcH2vdYdcEJbP3WA4veytPfkNZQid2NuuRb2m1aO5fBGRdXCAcwH3sMdkgAFLMR/GCvUDEahXDSKDx1SlhddOOWPMNC7LGrppmOOhUhC9py6miDMV9x11waCUjL9Nx85mJhLlasKQgjvsIsKibcNcMklTAoGpHOJra0pXVa16Nhk4ZTdIb707F95ZpF1YwQNtUPW9tYnDPRPV9F0tyLlnFfiCwTOUVkYVfVKRnmtG46eHXrHCut3LVeVMRpEYZgKLA8SUu4UklhZ0VYXnSfgVaKEV8tWmaZOlxeUydBM+rlXLdPxEVFkvdiESrGNZMKtIeW1EGQRF2zbf6i3OjKOQjBkNurTjgT1gj3JQTgDEcBXLjq0Mc4CpE5CxkeEWwUs+fau9nx3z8mnK5QWj/FcS9+Kst/5pQeg3Dc007k5m89OHQ8X7nW0ADVWNGwighoiEdBC55KHp5bORvTWRYISyNANoxQSYysoNhUFrZWFJethbVFha+EXfVeRwGgbhV3V0uoOG6mDLqhkgjGnrri+BEY85fuKDTHUC590Y/kmdOgZHg5pU1mpnusrzOAq4oeD5djKrGl5Kuk5LPFg+jnKKQiRt2r411VQ2ha978mFwEXydBdrw8+56W5VGFS8XEgNDxcDjlhtNhxnSYCj2rS7bId7VyM9Jy656SV6kjPpD0eLBCJUDMRU0lEIzTSwWFoba+wYtFqMIeF5Ew9FDGCSepMjbHc+F/38D+fuJndW6aZWDHCz/3S+TzrVedRKPXvKpohw2OFzFnIcNAI56rc8tsfZf6+nc3k+MIDu9h9zU9Y/ewzOffPr0D7Ho1Kg7968ZVsv3c3WhWAQUZDWF9MRZBcuNdYRYQiFsuoNuQ9GN0wxbIzj6O0YTk//ddrmxoNKYqeEMUwKLrgVsMtTsHGEc2Jox4Fz/Uq2F5NqhCsUzXsXaAqTGyJBrTCbjEWnCFdiIQ1BdhcbukZHBqkucJfrFAvNdb9ZIrznqLguWZN1dg5JcsLTpwpTtMz0jLd3Y5C22iuJHLQJJYgwNQ93mJIU06xFQqe13QwU+Q8xUTOpxIZoi5jHlvb5BIMEmzqOJZIU8ApPXrVxAToZhSig/SIIkr0E4a5Cqmj4tRMLT/50WY++3fX8l//+iOmdy40x9zx4H7uuXErX/rwDbzvG29i2arRReec4VFGlobIkGFxLDy0m13fvIOdV99OfW/SaSj54ktSa77n2rt58CPXsOHlF/GZX/hbziXi/PUe042Ym6b9rhJDt+9EIGwcsSgl3DmtaeChSCsEPPY3PJSCsRGf5z39bNadsZKf/uv3emxRyTPMx4NrH5VSRNZy4XKfiUAlBMfOQTwFKMuYr1iI6SHNgYs+NOiP9vVxmJSBnrUMbp+GzrL+rnIEOt8b8WmG86FN1EilRL5h+y+OQKtmKWfdWsqR1xzOU87YGVg0MjDovvdIbodLOZtUj2F/I0bjJSqerT0DDb5SjOd8jAjzUdQql+xIt/SfYaBcfwydKIXVjaFqTHP7etJp0ooQY4nbRlJCwk3oTE11n1+Lo+E8sjiy/Puf/k/XryJ9Iux4cD9/dsW/87ff+a0lXKEMjybEOrGzQx3jaMTwWFmGDLiUw53v+iw/ePWH2PzJ66jvmRsc8xd4+HM/4Lu//PdMEaGVy2nnPLhgKmZdwWkTKISSByeNWE4ftzSMu5mO5twttqCh5Ler+ykWds7yhd/5Av/1x1/rOOR8JHxjZ8hVW+s8uODMeGeI2P1trOW8KZ/leYWXtBtut4EFz7I8F3NcybChFHPSSMy438uUXzSk3vXveABPXQ7rijRbbrfPqx82jrj9qzHsrGo2VTy2VD121vRAEuDBwCQRHIsjPabNkCDth+G0AB65O3JwUDiCaQpHxpSOagZPOaLpTBhTji2RWGLpLWkcDQBcCqRsIudW9fnMrHRGBkqex2jg4yuFp9x3pOR7LM/nWqWfyX4NDFGXyyG4fhJGWq9rpZoPpTo7V5rEYfDaSjj7fbQW4f4f7+DLV96wxKuZ4YmE6667jhe96EWsW7cOpRRf/vKXO94XEd75zneydu1aisUil112GQ888EDHNtPT07z61a9mfHycyclJXv/611Mulw9qHgftLCw28Xb8xm/8BkopPvjBDx7sYTIcQbjrz7/Irm/eASzNKzbVELNQQytFJVbsbXjMRhrQnDxqefoKwzNWGi5YZlhfFIwoDIqFWLO+aPiZqQaB17qptuD+vv/m7U31vYVI+Ny2Bpsr7gb9cDXktpkq+0NDbJ0xGPUsC6FhtcuEIH1M4KhvWZ53zZzSEsSCJ5w4alhTiDu27c5VdyMNdOd1myHy4bQJuHQ1PGs1nDmRnlGvU7O6AGsLMB8pdtU9ak1+hHO8GqZF6hyGQVLMoRWKnmZFwWdFwWc88Lo0LRSBB7me7oh9jnGQr/eDh2uLnfMS4qQJ2RVW2BmW2RGW2R1WqJqIlQXNdGh4YL4OQM0YdtUaPefptzWgStUa+31msbS6Rea1RiuIrHNAmtwG5Qz5ZOAiVhpHWh0UmQBcxKFt7G5I4gTFpCWsi/+mBOFf/vAb1KuL1OFmeHQhcngeB4FKpcK5557Lhz/84b7vv//97+dDH/oQV155JTfeeCMjIyM873nPo16vN7d59atfzd133823vvUtvv71r3Pdddfxpje96aDmcdBpiHTir3vd63j5y18+cLsvfelL/OhHP2LdunUHe4gMRwjK921n0998iT0/2c6IZwm0pW40NeOzmDmILWyt+VQ7ekIoAiWszJumURZx5MZINILry+DkmAeH2RVCRMyTxup87IEc9S72/kxomAldzaIGfvPkgEDnBkYEAi2MB0nBZNsm6d+rC5aFyFAxrmF1NIR/kHIFjMBds8JkzpWDpjLIoXVS1+M5uHBK2FqFPXVLbF2IfVVBOH3CpT/2Nvp3gWyd7eBr1G8VICLNElWtWw5H3oOC77mGes1zdw5DJLYZVu+HpZm6/nNRyoKoply1QZiJ61Rsp3MWieVAXKc8G3WcmSDsDRuMNzTLckHz8039WYureIhFyA84h0gs+aTHRSti47QePFyDLqUUvlKUtEdk7dDOkikMlhiF35ZGghYxtN7W6bJfRMGK4JIfgkI1Sy/f+/rP8O5P/+qix8/wKOFx4CxcfvnlXH755X3fExE++MEP8id/8ie85CUvAeATn/gEq1ev5stf/jJXXHEF99xzD1dffTU333wzT33qUwH4+7//e17wghfwgQ98YMk2+qAjC5dffjnvec97eNnLXjZwmx07dvCWt7yF//iP/yAIFtHPzXBEovzTrdzxpr9n9u5tLMtF5LXBWIURlXzXB3/hjYXN1aCtQ2PLMEUCu+tec+WnlGvelNeWkmcAoWyGB8AFxf56jnVjFc5cXh1qrCzwwILgD1klj3h2qLMvAisLTg9aQ5Ka6LeDWzUYgZmE0XjvnJOuXgiFbRXFw2VhV01YiKAuioYRanGiIxA7MuS3d8KmhcHzVc0UQXuWu/WYDISJXK8JiqSdaNnJ+geF7robGOtWvsb2X5m31Bf7XIu2FVT7vpKE/mMREEXBc21GUxHmqaDAhNef+d/AJORBRUHrZl+NBys1ttcaRImyXvtHPer5Sf8J2zx2+1x8pch7/Rs4GZyOQ7ptyfNRqCVVvqXRjDg5tkkIkZE1GOj55NrnFEpMnYgo0WqIMNSICCXmR1f/lOrCIMZMhqMJ8/PzHY9G4+A/182bN7N7924uu+yy5msTExNcdNFF/PCHPwTghz/8IZOTk01HAeCyyy5Da82NN9645GMddoKjtZbXvOY1vP3tb+fMM89cdPtGo9Fxkebn5w/3lDI8Atzzx58gjmHEj6kbzbZqkenIxySEv4I2LM/FjPhpoV4LM6GmMaBiAFwfhflIsyxnyWvLqB93aClsq3ZGFqwIYdIaIKedEqCnnGDTCzc2+O/teaoDOjpqoBwLU12vtd/wg76tpdtmrKDkCTklPGksIqdhSwV2VHWbyqQwEQg7KkKiOIyIMBMKd843mKnlaVjT3FbjVvSVtkV0ajQigQcWLOtHFCW//8TSsr7lBSdKZMWR+0Z8V73Qr/oiXHRBrPC101/QSlGOLZM5nwONmILWeKpl+FOD76ohnGOQ0x5+4nEYhLoxKBS5tn4PgqtKKGhF4Hk9htdTmkk/T0577It6Fa2m8h4r8/mm8xdby95GyK56g931Bvnk+KNejhHPZ8wPKJuYSCySrNDbkfe8ofoPhk5nx0uIj0uBcheqdY7SIjdqNLaP2xG1iTl1I8ZFYr551S289Nd/dmmTyHB4cRgjCxs2bOh4+V3vehd/+qd/elBD7d69G4DVq1d3vL569erme7t372bVqlUd7/u+z9TUVHObpeCwOwt/9Vd/he/7/PZv//aStn/ve9/Lu9/97sM9jQyHgPIDO6jsXSCnDQ2juWdhhFrXar9uNTvqeVblQiZznSvzA6FHoCwFT4hFJZoAnaH0soE12jCZMz2r+uU5Q6WmERHmo0RVsO39oiecOuHycb6CC5bHXL+n/2rUkdtIihndHNqlkPujXUipNe8TRyJ2VDXbq4rQKoqeZVVemMoLY4EjZUZWsbCg8RAmi056Z0+lt5eDxYkqDQvjH6gbSqODg3+uSRaMJAtjZ9icAzIXuWturDAXGsqxC60HWlH0dIdMcycU9dgiYqkZywRCQSvq1qIt+NpVlNSM6VkR12zrtSbBDzDiodr09BUQaK/nvCNrKZuoObaPR9wW8j+xVGJZEHQYdl9r1hbyFD2PLdVqs1dDLA2KnodGsTpf4EDYoG4tMYZU1dtTqqPz4yAYcedtxLp9Et5Cei6pNkO3AmR7XM3SqVHhiJKtY9ikTHOQo5AixnDLNfdnzsLjhcPoLGzbto3x8ZZMaz6fP7RxH2UcVmfh1ltv5e/+7u+47bbbFmWMp/jDP/xD3va2tzWfz8/P93hcGR5bHLjubkDhITxcK/Y4Cg7u+d4woKgb5LzEQIiwoRSxLGebq/VqrNhWC5gO275uIowFpq/w0JpizPZawL5Gr0ohOPXBHx8ocOFqRc4TRoPBN1jB9TDIa0slOQ+lHKkuTYXUjCuHVEoYDyLG/Bg/6XxZMx5zYUDZ+Nw27VFta4bVsDAbKWZDy6WrIiyKNQXFvQsxK4uOQT9dN0tdiPagbpyeQP8UilD0WlypqoFK5ISjUjNWiw3bK6bD/ERWqMaWscCj6A82lOk+c6FhKu8joaGR7BuLRbdR8wTBqrRPRud62eLY/F7b96eUpB7aUTMx+6PeMKyHh8Uy7ntM5fo7hEopluUCZiKfOSe0QSzCnqjO8iBPDs3qfDFxcmKqxhCKxVtiFlZwZauhMVSsK5ZUQA6vw9mQhLQYYTvknd159Ar3pXwESEs5l/ZNKZcfoYZ4hkPHYXQWxsfHO5yFR4I1a9YAsGfPHtauXdt8fc+ePZx33nnNbfbu3duxXxzHTE9PN/dfCg5r6eT111/P3r172bhxI77v4/s+Dz/8ML/3e7/HCSec0HeffD7fvGiH4+JlOHTEyc3IAgfCxf3JBeNhEy5D4AmTbY4CuEjAaWMhq/JpzF1YFtikLLJ3vJyG9YWwr6PgoJhteNyybxStYG+tf84Z4IwxzUSgyWtXspke35UHOlVI1+RKWJ2vMxlEeColo0HRM6wp1mmYOJlPe5TE/T0dau6cCSho4aRRQymI2BvV+HFl35BzWBpMnxuTy73DaCBE4jpV1owj4uWSvhBWLDuqZuA6dSEyzRx/OxSumiA9w5qxTDdiRnMekzmvo9QvXSHrtvB+d4MqoKf8cSTw2oigltjavo5CCo1mxPOHVqGICMvbnAnP0wSlgGpoaRhLwYOVBY+NIzlWFAJySrsSxyUy08tRxLyNMDg56IJzBzq2Ucpdh0LSWbJjfrTxFBLuhMESEZMoLjCoW2U3Vq6bWNJ2GZ74OPHEE1mzZg3XXHNN87X5+XluvPFGLr74YgAuvvhiZmdnufXWW5vbfOc738Fay0UXXbTkYx3WyMJrXvOaDqIFwPOe9zxe85rX8Gu/9muH81AZHkVMPvVJbPvcD4isJhrSejlFJIpIhNi6EsHuhXBa9XDCSMj+hstTjwyJBgDsruuO1EE3BLh17whPXVkhkDFyukFoEy6AdiJOo77mKcu8ZvRizLeEFhrW1bh7CnKeIxhqZch7tsd5Sed+zrI6d8zmCPtMW1BsrnicOxURaLh0JXx46yw5/E7xgAEYlorwdP/yO4swHXaWNyrl0jK+hj01R6obRhStRpaJLglEBRR8TWQtOaUIRagZS60auuiAdJZktrea9tAuzN91PloJK/NO9bBiFJEYdscVZk0dQchJQI5g4FwFYSE2rB0SrVRKUUiaWCkFExMlKFvynmLtiNd0bkCxppBjdd4Rr7dX4uT7MWhsoWaEsm15fWlEot8+qQPVbfjbCY1AR3olxiw5ygEwsyvjdT1eEDkMokwHWTpZLpd58MGWXP7mzZu5/fbbmZqaYuPGjbz1rW/lPe95D0960pM48cQTecc73sG6det46UtfCsAZZ5zB85//fN74xjdy5ZVXEkURb37zm7niiisOqlrxoJ2FxSa+fPnyju2DIGDNmjWcdtppB3uoDI8DbGzY/rnvg0A1KRVcrEzSU47Nvzzf2/Y5hVKgBZbnYsrGY9Qb/oOpGDXQUUhGZCHyuGnPFMsLAT+3ymNL2fbwI26dFi5ebikmioh5zxmQ9vPaUdWcNTm4fj1NWzxpLOTuuf55RYtiuqFZU7ScWPJZldPsD82izZNgsKMQaJgNYSInHQTBdHvToy7ZwsKw+s4E3Y2QNO5cA6UoeQoRTWxa0Qk75FxU6lXRXdwpBFqzfsSjFsOB+Qbb43nauzfopF/kYBaFomYWOR8FqzYuY3qnJlwIYc5tv7yk2xyFzvmKCCsKHvvqjj3bvk16QzfN6o3W8T0Gd9BMz3nge0lVRHfMR6EI0H2aW/ciXJypmuHRwuNQOnnLLbfw7Gc/u/k8Tdu/9rWv5eMf/zi///u/T6VS4U1vehOzs7NccsklXH311RQKheY+//Ef/8Gb3/xmnvOc56C15hWveAUf+tCHDmoeB+0sLDbxDEc39vzXLcze8iANq2mIR8mziVbC4Fv5uB8zF8LKwoBNEghOJXEyBzEubeFu5L3bFrQMjSyAc1J2VIqICDuqJNyKTtQMfH+/5hkrbdIwKZlJMmxkHXehsIjzIsDy/PCbdCVWzSjGCUWfvWFIhQYj5FlKL4J2KKDktwLd/RMGg7GU/hHgIhGBds5QLK3jrChq1gD76q4Nd8PaJTWD0qI6iH4KxQFT5zv7axS1R4iht83T4vACjzZ/pC/27q9gqrG71kmUpbsNd/d8i77CSIigCRKeQSqaZCVRuew66FL5WN1wnAbT4yh4aHJJCCpPQJ1o6DjrT1g+9P0MTyw861nPGhqNUErxZ3/2Z/zZn/3ZwG2mpqa46qqrDmkeB+0sLDbxbmzZsuVgD5HhccSOL/4Qi6t2AJgIbJuwUvdNUshrp5C4I/ZZrPeqAoxo6kZR9FzkYtTvJTm6lIVha3XY11MYD9wathq7vP2gozas8EBZc9q4oGk1qqoaRcXorp4NgzFsJQ/wUCXP7oZw+lijaQ4P2AVKOoezX737Fzw3v/bFRqCdo6CVIucNNk5B35cdaW4qB7PhcGer6CnGmoMIoxomOziEmpVFp+mgpcE3dy/NSLZHFZxxNCg0ZRthVK/DZTB4Q/I1nqd5yqWnUNxZZmFvGdsVZdCeIj+aZ9uOhQ43JFiCAiXAZODhacX2atSRLvCUwle9LIylRIu64TgKvREFgIA0feIqLQoSEHZ0nXC/HQ8PH82L35RVQjxuOIYbSWW9ITJ0oLZ9P5Ft3QhzWliVj+kMgrsve0FbTijWiQTGAmE2VIv+DsqRZibU7K5r7i/n2F3rdQhcLb5mWQ7GAqeA2A6tFYGC40pCUQv1WBggR5BAsbPmVBFDcTyMhlWJIqPC14q9dW/o3LWCh8uDnZeC5/oA1Izix7OFpvpiiGGbncZ0qf5pYDzQTOQ144FmPKcZCzQTOc1ooBMCZu+5p1eooJ0Spu4sYCSvhYIHK5o7Dj6pZfn2wRUlv33l7j5nDWwoNVhXcuWnw5BWA7i9ncpmgxClSVyG/pGZiLi5Tz8YY7nid57Fmz/7Wlad4lbV2tNoz13jVaesYM1FJ6C7oghLdQKnCj6TOY8NI0GTA6JwfBBfCXXpNPCG/iJVKbqdwnTb9DzboZO6ko7KCaUpqhwlchQJKBJQwHlxMZY/fPlHiKLesTI8BkidhUN9HIXIuk5m6IBXzCGVzpt60ROOK0ZUjCay7lZY9CyTfoSn3ZfouFKqwQfbq5btNVdxcPKIZnnehecfKnvMxtqlA8RxC3Y1AiZyBt9FjqnEioeqOQKlWD/SuoHWY2Fn1ZUIHn/OWgpbdjIXgUExkoORHFRjYV+9vxxz92tpBQQoCh7cP19g1apK31JOK7C37vQVmkX6bQYhUDDS9FbcuOuDCR5OtNlDYlYWfLS4KIZWikC3IgYF36leKiRJicCaIqwt1rl7vtCTjslrOHnUSWZPBoaGdWcSKJiOHIHU15rTJzT3zNm+6ZxlOd02Z+cM1QyIdvPwNKzKhzxprEpkfaqxz2njinsHcOtEhAhDhQakK+hk+Inlo+w4sAclmhx9yh8V1KROkUJHOaHnaYyx/NZ7X8z5l54CwO9f/RtsumkrD/5oCwAnX3Q8J190PFf+7ld6hg2tEFtpa0bWf94gbKtGzHd9SerG8VsEOrQVDBaP/mJOTSdCtZ6bRIXxYE2EI0u6gZwOg/t7YaHOq059D1986J3obtnNDBkeJWTOQoYOLL/0LMr/eVPP61q5aoJutHMOpkPh89tjdtXbb4uGU0cV507k2dfwUcqt2NJ9BMXeRsDaQowR2FzNk9OqJzed9+DEMcX4mesp2ZAHmsa+haIHx40otld6HYag7Z4aWxLOvmqeW2QDvrcnzyWrGk1/wIWiYV9d8dmH89Ri4WdXGbZXNZXYtbgueCrpc9FuNBTjfo5RHVC2EWM6R167n1q7+HlqWLRSaM9FSlbk3ZELnuXMiRqnjDd4YCHPXOjhK2HiuBWoPTPJKl81G16BYjyIUMqyt+EM8rK8zznLLHtrhn2N1DFSLMt5lHzVKn1U7mFEEYqlblwfi931PA9VA55y0WpmJ6v85IvXotTJ+DLafhaAIsYwR4XuTI32FC/6ladx192b+ME1dyG2ZfTaYZWlKjVyOuD0MzdiY+Gsp53AS15/MSef1WJsK6WaDkI7TrtoI9/+1K3dwzLdMKwqDr7NCcKOPo5CemZpG2+tNCI26dbgIkZBh3pEr6MAzlEUwE+6RAjO2bBtERiLoIdUZDTbWbdhfqbKpz/4XV79tucMPLcMjwIeQSOovmMchcichQwdWPPCC9n5pRsZJvviVt/i6vGT+1slFj66JaLaJzr6QFmYjxv8xkkx9y4UewiTB0JXOOZhW62Au+6bjngGc3fvYEec8id6Ge4ewvEjiv0NYbatwGFVvuVchLb3pqyVcOM+n9tnNOdMxqwsWCKruH/eY1PZI9V0rBnLaeOKzZXe43ejoDVlS/OcUjSMEzdKyzADBaXA9YlIcVyphlIwHlgumEo+Da0ZvXCMA94GNn3tjmaYPaeFMT+i6FvGA7eCTR2Gkq8YCTQPVUKeeu5agh2ziUR1Z/oCFBohsqqjPLQSabYsBLzlE7/Hhb/5Mv79377EPTdsQ+8vkbcFJleMsWPvAeYXanRF69GeojiS55VvuJTXTV3OX7z9U3zj0z9CW6+vw4CGl//GJbztL1859Lr2w8++9Gz+/Z1XU5mrd5S2VWJhfz1mKu8lTpnGGqc4WjUu6tLPUWhHerU8pVGSyie5SIrjyqZJOiGnWqkdF5Hw0QlpMiVPesrHiiUkpqg8qhIxooar9/WLS/zHB76TOQuPMcTS8z1/JGMcjcichQxNiLUoa7C+R0Esddu/dFIBhS6i2s0zlkrcP0MuwO66cM+8k3/uHs8D6kbjKU3Jsxhx/ALb4wy0XIRhGWOUcMKIZjYQtlSEQMPKooso+Nqx/t0YrXOLJXnEmh/uH6AUCMyGirXFfmTPXkwFPnVrmfBdV0UL1GJLJe6cfSQwFwp5DRtHhONHakzm+nhd1hIfmEP272dNoeHWpuLIqHWrsLG7fscVQ/Y1AkySHt1ejTlpNEBtn+lZ+aeIre253gBKK8ZWjgFw+hkn8d4P/F7PNru2TvP2X/5nHvrpTryEN2Biy4rVE7z/qjexav0yAP78H17Hm//opbzrN/+dW697AN3WhtzElue98qn89rtftuh17Yd8MeCPrvoV/uyV/05Yj7BpRMBTLETCBS8/i4ufcwpzuxe45iM3MrOvAmjmwsVbPrd/33SbYqNpRhoct2NEe0RpVCohNKaORLvUsxtTEeBTlZgIQyQxfhvRMR1DqVb6I40vuE9K0ahGWGuzVESGxwSZs5ABGxsOfPdOtv7zf9PYM0MREB90bKlZv8Mwq2Rd1R5VALh9dnhOVgG3z1nOmxCiNrGgnBJKnpNaTtdoCqeuGCZOQzdcyHzYGbnOgJM52CAuvI9yYkwpSU+TpiJaYy4FvhKmck750c2h346uT8SLVo1z/4KmEkMlkW7udhTasbcOY36VqfwA8ppWSKFIfecelIL50GNLJd+sXAEoeYYTRhpMBoYtFcW2iuHU8Vyye2qE3LZGpGnc0vPojpCKFS54+TlDr8najVN88oY/4LYbHuDma+/DxJazLzqRpz/3THy/k6G5et0UV37ld9l07y6+8ekfsWfnLMtXjfGCKy7itLMPTeb9tAs38sHvv4WrP3oTP/zKXTRqEcefuYbLX/czPPX5pzeN8Fc/eD3gHKRhn0c7RjxFpetLlzYk0yiW5/JoYCGOMQnRU3dJPrfDNQIDkphVmQYFAvIEHRUZsZhmJAMcSdIiiWqm94jLODM8QhzD1RCZs3CMQoxl5+evZ8env0fjQBnajGjr/qPIKaeE3x741zpZ6SRkwJw21Mzw1bbgQsIr8jHbaonxInUU6Ng3PX4OV1LYveJdSsov3WNlQVFMukrWrFC3iti6ttjuZu/WjZ5SLMspZsLBgwsK5dXYFYLvRZh4jN7Ii9t/TdFSN3AgbDk2jUWEhRRw63SOU8adszDd0Nw9l2e64ZHTwkljEacet54ffGMr98x71GLFaGBYU4SJnHMYqkbz0/ki9bjBbdPCBcsLCa+k9/r6CqztdLxU2zJaacWGc9fz5MsWF1RTSnHBM07lgmecuui2ACedvpa3PMIowjCsPG6S17zzubzmnc8duM3YihH2b51hfz1qOlDDoIB1pTyxtczHhgNhSEkHBCpgLg7RKMpx7Npda00ldjyFQYbciqWeFEe2o05EnYhAPHw8dFuxWpr2SMspLYIOhotDZXgUkDkLGY4l2Cjmjl/7f5S37EteaQX3LY7wH1vXfjklwXUjNZGesowGEVM5lRAb+9+8NJBXinvnLPsaMUVfc/zI4jc6F2ForXqVWkzNwaU1dOKAuLCte62gXbvqhlWUPEdeVEkLYUE4rqSZ6aOOV/QUI74iIub6+Vn2TC+w3C/y3PERQttZUGjFtYd+YEGhUR3Rh8VK+QTYVXOpn5v3F7npQJHYWiLrOCL3zvlc9Zc3I5JyKKDREA40YtYWNSeMpitNQascqwomcf4GEeecw9A+L0lSNCg46/ln8Mq/fGEztfBEwdN/6Tw+//7vOFEmpakPKOlMMRY4ImPNGqbDBhOeEwILtLCx5LgGVWOZCWPq1rVQH/QltWKpUB8ahYswRBhXUjnEAa9HEVvu3c0Jpy+9GVCGQ0PGWchwTOGBP/5o4ih034hamfyoDwmwHalbUPDcKvhnVwhf2D54HwvsreeYa7j1vEU4ruihvMH7pDLLpOFarZg4forZe/djleAPyNXmu7QJdtY0G0dcDMHXijGfhI3u9Aw0jjzpac2GEeX6BeAM6XEjPiVfJ4x0j/XqBOo2ouRBzoNRX6gby4FQJbyExIhbkhD/wa38fCU8tODz/X0F5sKI/lHy3jF31SyjgWJlwWlH5DzFstwikQzVO5L2FCf8zEZ+8f0vYdn6yYOa+9GCZ/7qhXzmg99DNSICTzOOz3zcP/WjgUgMdy5U8PFZ7hfJaVhR8Duu3aivWZn32VWLmA4jlzroU1pZJ1pyCWUrmqfaXuvc+3de9I985YHByn0ZMhwuPLGWDBkWRbh/jr0/eGDIFu7G5OnFSHyOF5A2X7p4BWwo0iUS1EIOjY+LVqQG9WCCcVZg87zh2zfv54EFeGgedlYscVtIT+HKJ1P/QwQaBnbXFfOhe56utJflFSXPUtRCLRZ21RWRdeH80ycD1pU8ThoLKLaR8NKQdUH7WPEZ84RRX9jXcBGEznRJL5FzMUVBjfDkyQbf31tkuhEPcBQGY2e1S0Z4iSHqiQAngOWDJ8KFr3rKE9ZRABhZVuTUZ57Y1Mwo+j6TQUDQdb2KnmuLrZUGUSzzivhasaLQakzV/tBKsb6UY0MSbbBd4k2p3PNi0KgkCaF6HIXuT3Rm3wL33Lb1kV2IDAcPkUMXZDpKSyczZ+EYgqk2uPMNf5s8G25IBqn1GYFyrKlEnWqNOQ1vOVV42nKaLZ7BlQUWlM+IzvessvbVpUd3vxOOa7AQGm7dL+yvd767EMHWBUFjKWoY8ehQclQKZkIX1bi/rJN0g0OoPcpGU47h4arqEO5x/AUPXw/KCbvX5iLNbJSOubhhzi8SRVEKzlteZ1tlMa3E/qjEknSadJjv1yKzCx7uswu0E4OaykO1mZ564mHn9r188P2f5Ps3397RYjrveUzl86zI51mey7Eqn2c8CNBKYUUY1XkQYcRvtecehInAo+TppiZD2pJ68RZRDgZDqCL3SJgKg6BQXP/1u5Z6+hkOFfYwPY5CZGmIYwib/uI/aByoLGnb1trf/RVZ2FrNUbOtFbNCmMiFFJLoQtGDXz5eeMl62FFzynk37stx33zQZ00EWyqWE0b7K+GlJtiKcO9c56zaEYsw04DjRiRx2FvNnGYaUDfOI44F9jdaksb7FoRAKzaXnaPQTXRb/Lbuejp4WjVZ8R3vKrdwbR9HK8VYoClHbWZDJWV0BZ83vHId+R/PULed7kd7Hf9SnYj8shLP/c2n8pN/uG7odq73RGvOADf9yw2sOmMtT3ruGUs82tGBf/zgZ3jfn30UpRVTZgMn+iuT74x0OIrtZT5KKRZMyJgqgnKiVktpqLWhmGdTpZZ0q1T0dpjoj+7PWBBiFSPi4XXF7dIURRQObzyVIcPhQOYsHCOobdnNzPV3kwTiF9lauTREUsgfWcX95bzrEtnexhfF1kqeE0bq+J5Q8FyIdTTQPMl34/xgr+7rKABUYrht2vKUKd1Wb+FGBpdO2L6IbyMo9teFEV/IexqlIEoivUXfPUSclPGBxFlQyokwIYLFe8ThNUnmOJ6jQwAqha9oK01MXtOKiZymYYWJ5bDh/FM47Wkn8swrzmd0NOCuP6jAHXt6HAWazwc7MiVfNVMPz3vnCzj9+U/mjFNW8IXf/c++kc+8droTvecl/ODK7z2hnIUvfuZbvPfd/waAH+dRymPe1iipHH6baFI70hRCSQeogwz1FH2Pi1aUeLgSsaMWYcQ5z0WlqdnhS8sOdyGZklEG3aVRkjKMnnT2+oObXIZHDLHSIfr1SMc4GpE5C8cIZm6425EJ48VEjVollAVtqFuPLZWgx1HIKWF1IaLoeeypjwDgK8vyQp2JfISxEFnN8rzw4MLg7oe7apYfTxt+dqXX1AvQuGoGpZyRb5+vEaf3L8l2vlbEKGYiMKGreFjRJYbnoh6Kgue0ETTC8pxlf8PpLlhRGJGhlQP9rpNOUhcjPsyFvddUKSfvbEQ6+Ayegqefr3nd7wfo085GrTm3+d7J734j6t/f4/bvcRRaXR76fX7rS5riZJGf+6Pnc/rznwzAGS84i9+5YCPf/atvsumHWzCRJSrXCbQM5DQoFNP37WXfjmlWrp9a4vU4ciEifPCvP9VscV1kDC2aGVtlpTeGstpVMNByENLvQWyFggqcNogIDeOali3le+IrzRnjec4Yz2HEfe7TUcwNB+YHiJclcYV+Q0urJ4Vu/2YoePLPnHDQ1yTDI8ThSCMcpWmIjLNwjMDW3dJX4wSQhmTPCZRlLN+gYTWxTdtVt/YIlLChFFH0Om95sSj21ErMNnLOkALnLLMDHYUUp49bjis1GPMNgW45CiKtng4iQt0Y6sYSiySCQk5+OTSGZYFBIUwl4ou9ctFuPnXjogpFzx3HU26FHRqXqmgai0WvqMv1Q9pbob/7pRRM5OD4knB8yXLqmOWCKeFZz9JorcB2Et7i0CS0tsGGvN97Fz3rJN74kV/m/3zvbZz5orM73htfPc5L/t8r+d0f/l9OuOw0d+4ojB1e8v35j107+M2jCJse3M6WTTub0RWFEzQyWDbH+4lxRNnY2iYHLTaW2ShiJgppGNvUTViI7JIdymqcclFUk/+yPBfwtGVjze6WKaTtv75Qbhs/IT6CI1AaZXjvb1/1SC9NhgxLRuYsHCPIr1sOVprkw9Rh6OTvCxpBKyE2GiOKmdCV4jXz2giTgaFuFA2ru8LbbqN9tQIWyHmW5XnLM1dHzX07IWwoCecss8QCD1c99tQ1deM4Eg0Lo4FCUITWDtQpiAR8HZHT4OleR6E5O6UwovAVLESKfQ3t9klDvYlGgrGpUFR6wN55e6rlLEC70ZW2R1qd4SIfeU+R0wqr4bizkp1HV3eMvGfLTP/Jdxy9cz4jYwX+4Au/xqnPOR0v17endRObrrmX/XXh4TJsrcDDZdhTdVUjHccQ+J//+MGiczkaUK83Op6H1NF45CRPXSLujXaxw8wwY6vMmCo7zAybohmq1hCmvSDEkXEjCzPJxRrWphogtFBN6ATtm67K53jeymWcMTrGunyJDfkSgkXUgKgCkAYcXOMpS4whJsaI5bYbHmDTPbse2cXJcHCQw/Q4CpGlIY4VGJPcsRS+ssSSWjppChgpXCpBKUUlDjgQBqBsc7uCFnIaQlGERoFxzYcmAktOt5WIAQthwGQ+wlfC01caluWEH+zz2Vt3d8OSJ5y3zHDhcosGbp8tUTPOfbHiNXPpvoaxwFAZoICc4scHAjaOWEQGS+ym8FXMveVcc7UIroIj0JZ6rIlQWCOIci2gK3E7scz1cMh7rehHwzqv+7iSYT7SxALlpE9GwyTbAdOhk6CuNzRX/VXMs167nAevvZMtN34VheLEp5/I2qedOPxEE7QT5uqVcEmr3e23b2f33kaPhHbVQLXq2mKnDRqNCBP1PoMchdh4/Fry+YBGw1nuGvMYYjw8ClIkUqFzFGwVjcYXnwqzTBOyinV4oigoH5tUNdRiTWQNkznNMN8sULC/AYUYxgPIJd1B6wbmI0VR5ygmKbN5GzEdD7ngSqhTY4YqPgEjjHUoPP7kpk2cdMbaw3C1MgxDxlnI8IRHY/e0C2VapwfQ8X0V8JRprrBjq1iInJLgqBdTMwHjgSStmKF9+WOBmUgzFZiuNtCdQaszJixnTISUI7eCz+uWbsF0w2M+bsU53EquFSEYWcK3VCBxNhbHXJQaf8WKfMT5y8scP9pAKwiN4p65IjfvH2FlDpbnYbohNMQ1BUpTJNBaLc6FsKJgGQ2EhRjKEc3UiwXqMQSeSqIWLvpxYC985n0HUN40koRM9m/aB5+8ieVrRjmwpzxwBdKdhphaM7b49bHCv/3qp4ZqN+ytw8YRmiqZE6PFRcc9GjA2PsIrrvh5PvupqzHGgoIDsp2VbEShyEsBaEVsQmrMsY+Ln3Uq11z7LXKNUQJV4Dj/ZFZ46yhpTd5T1K3rBlnoaVHuvsmpw1gzUI2F7dWQtaUAX/VWVKzLlZiJ67Q3N2uHh+Y0fx1zlNkdL/TyY7JmUhkeZWTOwjECf7yECETSLZvkSHOxuBVxlPATfAV764rb60UCBWsKg8L7LjhqEJbnIubDAHCiTiKdjaBEnOGPrcK2vb4/9Gm/QcYWgrZv5lL98Kpx9L+BkVwRwLKv7s5xfanB84+bQdFqJJXzhLOXVTlxtMHtB5bRsB7jvmVXqCl4dLSzF+BAAyIrlDxN3Vimw96jW1rOjxFXUpl2RZa23Er690SjzoElnrTSiue+9sJFt3vg2/dwoF/JRvs8xeXZS76LlJz7nCdONcQfvPP13PiDn7Bl0w6MsTRUld2ymTGmGGEChcYQscA0c+zmRa98Bv/8kfcwOzvPzTffjrWWpzzlbPJega/84w3c8K83YY2T4o6saxPuASjVky4TEeaiiDmpUq4Jp5Ym8doIwyJCQXuclJ/gocYc3d9iD8U6fxxPa5bJGHmvwK54oXUADRc840mP/kXMcEwTHDNn4RjB1LPOY9M/fKO5ou6E6sipWoGbp/PMRM6ojgWLWS7FXORz6kQZX1um63lG/Yj50Ce2Gj9xHCKrqMWui2XRM61eD11hcQtJsyf3/oi/tIiBQjHdgOX53uZU4G7Ot882yGnNuoLm59bOdTgKKbSC0cBwyniZTfNjGCuM+jhRKJVKAEMlcumIDSMKrQFxZEml3PzbV/HpejF1GIZhqqBZd8FJ/PD6LdhkkH75ce0pVh8/xQveeFHztbAecdtX7+b2r99NbaHBmlNX8vT/9RTu+/b9fa6XsKogrCm4NFLDKhaSNIpRil9650uGT/QowrKpcb78zb/jyg99jk997OvMzZaJqLPb3Ec1nsZJf3soLazfsJK/+sDbAZicHOfnf/7SjrFe8yfP45kvPYePvfUrbL9nD+D4CYHnOkkqrVyoOSlbmTF1NoVzTm3BKn5amWFlUGTKzxMZqBtHrPS0xym5ZWyL5gjwUQpKKseozqEScajpuMaCDfHwmtUxK1aPMzbxxIgCHenIekNkeMLDWzaK6arTbkeTwCiwp+4xE7WSsb4aFBztGIHYKpblQ7QIVeOxv1ako41h2wgLkUIpy6jvHnQdoWEdgdAHxgJF0XPh3EHwFYz6MJUXVhacwY+tUI3dfhbFvfN1dtUjPAUXLReK/uBfrVawplRnV6XEvHU6EKuKThK6FruSyKlcZx8KpRQrCq3noXH6C05rwZUq+sqd2zCIEUqVGh/96f/H9z5/B7s2H2Dr3Xt48PYdNBLGnPYUF7/oTN7wVy9kJDEU09tn+Ydf/iQHts6gEu2ArXfs5Eef+TEnnLGy4xieEs6ZjBkLWlGPggiTOUPdwJN//0WMTY0Mn+hRhsnJMf7gna/n9//k19i75wDveMf7+PSnv4QR98UyAs+97Fn887/8NZOTE0PH2njWWt75rV9nyx072XX/PvIjOU6/5CS23LadG794B7O7Frjrtq3smJ9jnyygUORc7IFILHvDOhJ3RdQSL3K9P5k4lu1ROWFPVKEhhhxBUx1EEOZ31XjTz/8dH7/u/1Io5Q7jFcvQgyyykOGJjnDvHIuZe2c0hO1J58N0+0hUn0qGTigcQdA1ZLLsrpSc+JG4d7tTGLORT9loPFyFRskTqoaOOYYWQlyEYcOIx/3zhm6nIt1jVUFz3nI6IgWegrHAyexevbvSNNJGIJIYK71RhRQPL/h8d1eJTQuKWFx3zVVFYX1JM57rt5P0cCYCDSsKsK/uoh6j/uBKjd7hhLGpEi/89YubLzWqIfffup04Mpxw5hqWrR5r21z4l9d9mpkds83nADZpi73lnn1o1araeNKYYTT59XcrOBZ8RfzTrcDPLHGyRxe01qxZu5J//cjf8J6/+ANuuOEm4jjmwqeex0knH7/kcZRSnHjeek48ryWKdOazT+HMZ58CwMtO/1NqRihQoK7qxMT4+ICwXI0w0HGnlwNRtRGhGPIEXds6BdG9D87wyb/5Nm98xwuWPP8MGQ4GGSvmGIFXzC++UYKG6YxAzC+qJissz4fNCgbVRuByjaOgO4qeykYbFLEoxn0haJYrtpcsuhRGJY551YkVLlgedvSeyGknoXzOlGqKOaVIa9zHA8WTx92Ky2CoqzrXHKjysS2GB8udE1uINB+5b5J/vm+S++dzTc6FEdhVFW4/YKj1sARbUtPtSLUdxgLHhi8kUYjFfnTKU5zQpyoiX8px9jNO4vyfe1KHowDwwA+2sOu+fdgBOY60/w24/PrKZqqm/8bb/udOavsXBmzwxMHq1St5xSt+gV/6pZcclKOwFJx3ycloT5EjYExG0eJhxFIkP4RZkxJ8u76XcUiQrO36iXUB/Oc/3XBY55+hF2ka4lAfRyMyZ+EYQW75GPnVkwyjC7pSQKd02B5JMKLY20hvUL2G0lfCxtFa85UWB6H1b5J5xykmCjklSKKbb5P3i1pYllRdaJzzUPQET1n+95PmOWtZxMtPqPGu8+f4vbPmWJlXlHzNqqIiGKKvoJXitLGAhqpR0VVCFbG9EfHNvcIf3m342wcMkRUaRvGx+yfZsjA44BYL3D/Xng9xjkI4oKW3Ss6hPV3hDdEOFnGlWRe8enHSYjvuve4hdD/t5rZ5pHyJcX+Io5DOwwrTd2bdDA8Fr/z1Z7gS3OQ/H58cOYpqeKog7iqtU0DB8ynpYGCPCYUirMZsf+iJ2wTsiIDjSB/a4+isnMychWMJJ775hQwKfaYrTwE2lAzdqoszoWJXrTsdIUzmIs5eNk/Bc+6yUrAQdYZKwaKVEzJSOH7B6kLMhmJEQTuHoWEVuxqaA6FGxPU4CK2iHHucPlGj4EmztNNTjpvwSyctkPeEMX+4EiFA3tPk/c6NUgf/B9PCvz9suXl/numGXjSlWI6hEiXVHlbRMMOTNEopCuOtyE7DCgfqptmNMEX6d7R2GStPWdkzzjBYY4esVdu2gyU0SU7nc1BTyNCFMy88gd96z4sRICJqlmb2SzO0w0gr2eYrRaA1q3IlVuULrC+UGPe7f18OgvAXb/w0Lz7hnbz4hHfyh6/6KDddc99hP68MxyYyZ+EYwspnnwPnPzkp/5Pk4d4TaBIgV+UNy3MpP6CFuUixvQpnTMzx5Il5Llg+x5Mny02ioAjUYo9anC6jBV85pn1LObIVAfAUrM5H5JQlFuUU74x71K1K+ikIJ43V+3IL1pZifuO0aY4fDRflVICriR+E/9kr/P3mGruj8qLjgNNeqBrHq1jsyAK87uv/hzd9963cFXnceiBmU9nw09mY/Q1Lw7ieA/salrtnY+bk4H+WJzxlAyZepEFRqgsRqUUdAaUVy8/deNDzyNCJX3rzs/jw/7yFibUjxInDEIkdqv4oIoTGOj2Grve0UkwGOSa7HIZUKPre27axMFtjYbbGj751D//fKz7Cle/8+qJqkxmWhrR0+lAfRyMyZ+EYwsLeBa791lZ21nJEVpMmG2Kr2iolXO+Ip07V2VCMeyIJBQ/umxuh4Fvyniv5Sr/8lchjW7mEJITIgmfxVbNosGc+qdMwHpjmFzGySfVC4syERhLlu/7IeXDaRG3oSk1E2B9GVMzi3f4aS1x3u7xy6igMv/FHBj75wg9x22s/yAtXh5wx4WS1q0bYUjbcORNx50zEw2VDQynWnbR8SXNox9k/fyrjq0ZRAxibIq2SzYZN0koK5iLDdQdqfGbHAp/bucAts3UaIhx32VkUVywu9pRhcZx90Yk891UXEAeO/FMVp3cxyIArpch7XvPvfhjzg2YjsIEdJZIXP/uh7/H9b9z9yE8gQxPHMmchq4Y4BmBrdeyBaX76pbsY9VIlxtRB6IZbdQbK8tTlNc6KNXsbPkYUE4Gl5FmMCLcdmGAyFzHqxygFc2GOuvHSEViZj1Ckof7BhZdKwYhnySdS0iM+lDzVlFJWyvEB2uWkuzHix2gMRry+uXilFLfOLx4x8JWmLhFWpKkuOQxpp0oriqCP7K+bv+NT7JsxbKqHbBw1XLYGThuDr+3QPZ+BjS3P/dWnLnrsbniBxxv+9Zf40Kv+nagetQn+tOSm2+9R9857bK7Nc8O065uQXt2HazE/nGvwd5efe9BzyDAYl1x2Nh//+6upqgpFKbEgDcZUvqM1dvq3FSGne1UeuzHi+czHi7KPUQq+8E/Xc8kLzzos55Lh2EQWWXgCw84tMPfPn2Tvr72V/b/zTo6/9nNcfsJuxnPR0FCYk/tVGKuxeKzIW1YXLAXPvT4X+eyoCD8+kOP26SI7KkWqscd85LgNDctQwmG/4ylgZR5KXncpn2J7uTCUk+BpmI+kpYrYFe67b85wf7U2eIAEAR4CzElt0bDt9qrw4LylYZKuk4ucX14LexsBC6Hbcn0JLlrRdQwFl77yXM555smLzrUfjj9vPVe8/0VEti3NRK9AFMBsFHHddL3vqrRhLL/3mo8wP1t9RPPI0IsLLzmd08/eCL5QVgvMMM9eO09NwuYnoJXCU1DwFncUgLbIwnCIwN03PXyop5ABDp3ceDh0Gh4nZM7CExR2foEDf/SX1L59PUStLkweoKRX96AfGs3+DonegoXv7cnxXzvzPFzR7KrBvXPw3d3Cd3YLtx4Q7puHhah1+1rKcYxATGfJZTs2lUuEVvd1GERgby1HwfPRyvVeqBuYi2B3DW7aBw9XNGNqeOloSQWYxEGYsVUWpDF0e3DiSpvLFk8LgXbqje0/qPZTSc9rUyWHJPoOZ0+2uBzjy0u8+o8u43f+8RVLboHcDxvPXU+YpHIicRoR3fcmEWFHmJZF9h5LBCrzdb7x6Rsf8TwydEIpxd9/+nc4/uTVoMB4MQu6zE61n4oNKfqaZXnNKeMeTxp3WgyLwUUAdc9NXPcpzByUnspwcMjSEBmecCh/4RuYvQfAdn4z91dK7jYkgw15P80AgB/sC9hZS5XjFMYK5dh0GKOZ0DLdgA0lWJZbTAbKHWsu8oit40v02yO0mh/uneTsZQusKLTCrkZgV6XAw4kAVIpAw0LsOv7lNKwvavxwkvsa+4j7uPUeihXeKDtsS1dgvy1Tl4jV3lhv0562g0UWDtSF9SMufTPsPqCUM96zkWZZzpLT8Ju/8VROetVFbDxjNX6/XMZBYtUJy1h1wjL2bpkhtq0KkvY5WIGyjRI5n/4QEX74rbv55d989iHPKYPD6nXL+Px17+Y737iNb331FirlOiuWT3D9Z+5hfQnWl7xmRGshUsxFg9N34EosA+URKE3Vxox6AQXtNdU7Q7GUTUQsliiMed//+Sy/9ofPZfWGZY/RGWd4IiFzFp6AkCimds31PY6CCJQbjkW92ELDkfdaEYjphmJHrWXMrAgLcdx3/VM38J9bNa8+0aUuXF4fup0Bp08AO2peM+UwCDXjceO+SRo2ZiofU/TAWg9Lr4FVynWLnAosuxo+vhJKvodiFXviBQ6YKhZBo5jySqzwRtgazvWMc1zRx8Rq0bLMmVBYP7K08mmFUDUaFQr3zvls//gdyMfvZPVJy3nOm57Gz17xFLwhegmLjq8UL/m/z+Zf3/yf2KQcVqvWlTXGstfOLEG+G+J4qUWWGZaKIOfzvJf9DM97WUsd843f+EPWFd1nnjqia0vC3BwdnIZ21IxtOqYF7VHwPBdRSLZVSpFDM+XnmY0bVCXim5++hR/9zz384zVvYd0JB0+izXB4qhmyaogMRwzs/DxS7w2jt0cMBn3pm6WUyf2pbhSRVWyreh2VEQ1rhxrHqoGfzrlBtHKM7bhNuMiKa219fzmPEYVIGlkYcl7AfOSzpVxgTy3X11FojS/MRIpx33LiSMx5U4aLpuC0wgTn5Ndybn4d5+TXskwX2RbO05CWYVRATilOHyst6Yd9ML99AcqR4tu7c2yr6aaexZ5N+7nqD77BX7/gnwmrw7tDLoanvfxsfvEdP4/SCq0VaI1NBJn22znuiTYT0xjEoQdAa8XZF/aqSGY4vIgbMaeO5nscgoKnOH5EEXd9Aa0IlTimatz3VeG4C+2OQor0+bifQxBCiZk+MM/73/zZR++Enuiw6vA8jkJkzsITEKpQ6P+6gsBzN5mmmFjbvSj9O5FXApza4ELsUe/qexAuUoYIinsTZ6FhFA+UC9xfyfFgJcemSo77y3l21QN8DVO5mJw2DFvrOg2H1vP8kIh9ZN0xS55CK9hW9dhZ06wsCM9cbbh0teHiFYYNYyHbojnqEncUdxa04vkrJslrj9ISYm+TQXrGi6/WQXH/fNomXHW8DrDlp3v40HP+ntrMoZELn/+bT+evb34rL3n7s7j4FecwclqJ28y93B7dR4yhyvxQyWGU4qWvveSQ5pBhcey+czs53f82vCyvyHuWmTBiPoqZiyKmw4ha228vpx2TeBDPRSmFpzRF5Tc/7VtveIC3/MI/sH9XbzQtw3Acy5yFzFl4AqLywE7qodezKlYKVoy4qoDd9YDIur4NJqnBdw5Ey+SlVQrg9Aw6FRcWx3ys+PFMji3VIGHja0KrqVuNTVZCAnha8TPLK5w9UaHo9Ya+U42A9q6ThQGllMY6ouGIDyXfdaJcWXBKeJvKHrG4Pg0rCnDWuOZX10/xjGWjnFLK86SRAs+aGuOX165gbcHHV5ZTRhfXZz1t3DSv17D0jlPJFDwNw9yKh3eU+fbvf2HoMZeCZWvHeeHvXMrrPvgS1j1zBXPiykednkSVCnPN5805IiiteOeHf4W1G6cOeQ4ZhsOEw1M9Z074TAQQWtvjoOeUwl8CGVbEcRtS51ChuP37D/GL5/w5d9+05RHPPcOxhcxZeAJi7xeuY6Hh2haLQGg0W+dGeODAOOXYoyGWvY2AmtFJ+L/90YIISSMlxbqiNJviQqtsaxjyWlGPoWG9nrFbcKWYNaMY8w3nTFTI69YNVMRVHcyELZNd8qAca+YixXykqJskKiLOqUlVItvVInMaJnKwu413oYFVecvTp3JctmKcZy8f44zRAisLMSeONjh9rM6Jo8K6Ynreve7SWROWZTn4yYxlV1WIrZAKKXaXcAou6jEaDLt2rg321h9uZvrBvYte46Xi5y+/GNPGQRAllJlmlj2E1LFYBMvPPu9MPvbt/8vlv/TE7Dh5pGH5k1YNrVTwtOL8qYDJnGI00PhKkVOKEc8jpzV1a5iO6uwLq+wPa1SM0wnpRpo0DNCM6zwrvBEmbJ63X34lP/nhpkft/J5okCRleqiPoxEZwfEJiIU7NmFCzYMHxthd99lVLlHQwojvDNZMqAHhQOgzHgzOjyuVGnpXYXD2pOGOWfeVyXt6UQLcZF4TL6ljAVRjj2I+xgNOGGlw+0zJtahuk1PWuKhAoFv5WEFoWGdgx3xpihD1O5eC50oqYwt+2xjLg5jlubi5r1Ku3bZWEFmfS1da7plX3LfgHBNwwlHL8wpRAQ+UhbyGXTVHGxwJhNCqZi8MwXFNhUSkScFi0QqlYfuPNjF1yqolXb/FcP5TT+NnLj6LW2/6KSZZoYoS6lSoUwHgo59+F8953kWH5XgZlobRVWOc+OxTeeia+/r+UqwIdQNrSzlEhJmGpRq7aMF03CDsiGkLZRNRNRHLggK+Stx7AV8UIzqAhCXjoVBK44vm3S/+OO/4wq9y7jNPeQzO+OjG4UgjZGmIDI8Looe3U7n6Wir/8z3iHbsBl064fabAV7dOcuPeMbZWPe4v+9w771EziiiJFizEmv0N5wxIUq2Q92IKXoSnLNW4U2FwbUmxtuiMdaAUwZDowrKc4rzJmIZZ2lesXYhpKhcz4rm1kK8gSB7Lcv3Enty5CC68PyzgIQJFr9Uh0q36leNkWI3BiReFRqERbthXYlfdw9Nw1qTwsuMsz19rOX1Cc+KoZiqvCZQbY01RsaaoXROgxA8w4sZLGwO15jHMURBGfEEL3PfdB5Z07ZYCpRT/+ql3cN4FpwHg+x6ep9Fa4Xkef/E3v5U5Co8Tfu4dL8AfK/R8L6wIRlot4pVSjOdc38kFE3U5Cm37AXNRAxHBGMsBU2YfZaalwrRU2SsL7LbzhBI7h9kK7375x/ndX/xHNt+3+9E92QxHLbLIwlEKc2CGmf/3r4Q/7TQo+aecxe2NSe6Zr9BOUixowQAPLChW5GnmzXfWA6pGcdp4hVWlBl7CBRCBuTDg4flRysYDUeQ0jAUuNGqSfhKzoWW6YUh1mAINK/Me5y0TRn0hn3SVHJajVwjjfou9qBUsy1tmI50QhxWeSnP9g8YYXk2xEEfcWZllbxgyESieMjbOxnwBrZxcc2RBKd10NvY2fOYiVwFyXGQZ9d2KbFfDJ68dcbIZxRDn1EwEMBcO7ygIQmhhkKYEKNYUXAvpu27YwvP3lRlbOTpkvKVjctkYX/zvD/Cj7/+E//rqDVTKNU45dQO/+L9+npWrlh2WY2Q4eIysHONVV72O9z7n71lX0OS0IrbCzpqhHMN40JIx97ViRVGxay4eOmaMEFrDrK1SpVcSOsKwx86zRk8QKA8tcMc1D/Ir33svH7jq17n4sic/Gqd61EPkMEQWjtLSycxZOAphK1X2/9H7MfunEYF66FOPfKxVzHxzCz/dvgxQ+Eo4cSRmTdE2xXkqMeyqeRhSHoFi7UidtaP1jmMoBRO5iNOmZvnGtpXUjIdGmj0aPK3wUKwqalYWvKazkFOWDSXLqA+Cz2njlvlIUbWa/sZRWFsIaZcWsOKaW60tGnbXvaQBkrC35mSdBcdbWF5QFJITs8l+NRtyc3kXt5f3ULMxK4IiU/4491frrRr0Oty4MMsphRK/umYdRU9hLExbJ/ec18KBhkriFYo753xOH4vxtcKKahZsdspSAwLjOUXDCr7q5zS4i7Q8J8yHjovR7eAcV3IOWWShboQ7vn43l/za4VvxK6W4+JJzuPiScw7bmBkOHatOWsFL3v8yPvDrn0+USN334vjRoOd7FMuwotcUwpypUVWDe0cIMG9rLPecM1ogoBKF/MFrP8LXf/oexiZKj/yEnqA4HJyDo5WzkKUhjkJUv30DZt8BTCxMl4uU6zlio7Gi2bRQQCH4Sjh/WcS6NkcBnJE9ZcywLHAtqEt+zKnjlb7HcXl+yzPWzACuUqJm0lW1tG2nyGnX7GnjiHMU2u9vY76Qb1YvSNsDludiNpRamhBWYHvVZ2vF5fzXFixjvuHhsrCrJlRiqCbqjPfNCfvr6biKnY0qH9p5M9+e3cy+uErZhjzcmOO2yjZqMuc6WdJSWXyoXuUze3exr+Gzs5FjJvKYjTz2NAJiAgqJVxCL4q75gAfKvnOxBvzWlXJtt6fr7bfyzvNNKzXOnIQTR4VlOReRWFOAU8dgec5llecj0J5mfu9CnyNleCLiWb94Hu/7+hs4/zlPar5mkh4f7ViKqRFYUv/UCmFz/PQojVrIf33mpiXNOcOxg8xZOApR/c4PQIT5ah5rOysZUj2EDSVDyZMew5Y+X5k3+ErYMFJbjGrHqkLIulIt2V+hVdoVMr2RuceYLz2OQnrMSd8yFcQUtVDQhtX5iLMnypw6VmuWG9okv7+5nGdd0XWjXFOIOGU05NKVEceVessYd1SFcuTm8dn9d1G1Ud8ST0NIKJ1OkQA/rVbY1UhJnp3pktUFCNpKNO0A8mTHmOIcgkBByZMm36LgOaeg6EEozqlYmYdTxuDUcfd62WgWYkUlcrwKa4Tx1Vmb6GMJZz39RN79uf/NR+96O7nTNVuj2Z5tCp7ukfHuhkJhl9yxyH3H67jfgVKKu27efBCzPoZgFXKIj4MVZVpYWOCtb30rxx9/PMVikac//encfPPNzfdFhHe+852sXbuWYrHIZZddxgMPHD6+U4rMWTgKYecXiI0iNr0liSXfGe51RdNh2ESgEmumGx4HGh6VWLM6bxjzzZKEhM6aaq1wnZPgZJ1jcSHzNfmYjaV4YD5OqaR8MbDUjetsWNCdN7PZUHP93hJTecWGUshUzpBPqjjWFIVnroq5ZGXcoSQJsLcubG3MMm2qQ2+PEb3dJBXwUL3f6t2RESaC1iuLyT6nKPhQCtz5jgbuUfQS2eXkYoe2pcuggHxy929YlURw3E37vKyt8DGJ1euX8dlr38Vv/r9XITndoYWhlWJNITdwX0GIiRG1eGxB4TRILEJZXBdSYy3lhcW7tB6LaC+HPpTHweANb3gD3/rWt/jkJz/JT37yE5773Ody2WWXsWPHDgDe//7386EPfYgrr7ySG2+8kZGREZ73vOdRr9cXGfngkHEWjnDEB+aoP7Qd5XkUn3wiupjHW7GMxu4q3SQ5EThtrMGGoqFiWlYutIp9dT8RXHLf1Kpxe47HS/kKCGtLYfN4Sik8LYRRYvSSoKcRtWjPCXCh9p/M5blrNseaootwzEUe06FmY0lYk49c2WHbWOm4G0vCzITl7rmWXkI5ggWZJWUZDDsPi8Fr+9or6JHUbb6nFKM+7G+4895U38cD85vZEc6ggOPyUzx15ESOz69o2ychgQ69DirhWEjzvJRyxxDcZyPAZW95BqMrRoYNlOEJjFw+4MWvvoSn/+yT+chrP8X0tlm0rzGxZU0+oBIbZiOTMGta331BqKoqPhqGSKIDjOs8omCXmcFgKCkfD82N37yb1zzvL/m3r76dXD4YOkaGRw+1Wo0vfvGLfOUrX+HSSy8F4E//9E/52te+xj/90z/x53/+53zwgx/kT/7kT3jJS14CwCc+8QlWr17Nl7/8Za644orDNpfMWThCEc8usPcfPs/C9bc3XVGVz1F6xvnsv2uOuFbAV600g4scBNSN35YaUBiBvXW/zYS2ORcIW8oljh+tUvAHlGEJzDRy5P3OVUqHTDQu/aFgYOObdgQajitaHiprtlVbNyKFMJGLyXmDDb5ScPq44adzrb4KQnujqoODBZb//+y9d5wcd33///x8PjOzfa/rdKduWbYsW+7YuGAMGIypppmW4AQCCRASh+8PAgkGQg2kgQmhhRBaCqQYQrHBxmAwxhX3IslWl+50p+tbZ+bz+f3x2Xpl72SdZJV5+rHW7e6Uz8zO7uc97/J6u3O3rxYC1qRCfrh/K3fntzT9KG8vDbOtNMwlmZN5RvoEjDEEBhLz+Ygb9i0r+yjruiFnknFe8q5LeNZbnnnAxxNx7NG9upP33PROHrl5E4/f+gQD20f4+c2PMOKXKBMgECgUBkNWeTjYhGJfhHSQYMrMrqWiEHSqBNv8/cSFRw9tlPCZooARhq137eV15/8V/3Pfxw7zER+5LGaC48TERNPrsViMWKz5tygIAsIwJD5Nwj+RSPCrX/2KrVu3MjAwwGWXXVZ7r62tjfPPP5/bb799UY2FKAxxBBLmCux492eY/NV9lTodO4kExTJ7fvhbSnmNNrLpzrusJcXQ2n5CiJraYi6YrQ9BFQFGsHkixWwTbdVl9sREmnv2deHJ6uuGUs12MKxNB5yQNngNQkfzkXKqJZV1tpQHeagwSDiPny6u7N174/Plsc55TQWBQDbcaQkgIRVr43PlBRgkhhL7uTu/pfKKaXjXcuvk4wz6Y7VEz/wCmzVa46pRylqwfMNSrr3z/3HJH1yw4HMZceyjXMXGF57Cqz/xEv7422/iT7/6RtqXZhEIpuQU43Ico8p0u0na3QR9ThuucSiZMl0ygZwerhQuK902xk2RtEiQJsk+xtjLfibJk6PIBDm27NjDq5/5wZqQ1/HOweYr1PIWgBUrVtDW1lZ7fPKTn5yxv0wmwwUXXMBHP/pR9uzZQxiGfOtb3+L2229n7969DAxYXYze3t6m9Xp7e2vvLRaRsXAEMvLdmwj3DKAIcJTGUSGOCiiH9YnfVLwG1Xm1GDg0TvjVxLx80PojFgJ2TSUAU5clrvxbCBT37e8gH7gYoM2xhov1JNhlNrYFnJDWuApiyk6u893h12Wk62wq7eXR8m5a97Ks44p6aKI3DqcmU/S56Zb5F65I1iZgiQ0VXN6+DCXmPkeONNwwsmfGj20jAsF9uR2oypjiyvaoaI1BVA51f0lUDDPDa7/6RpxY5PCLaM2zXryR/3n4Q/zDf7+DJV0dYKBTpmo5Oe0qzoleN51OGldI1rgdLHez9DsZ1rjtLHOzjOkCI0GOBHFGmCDP7DHuTZt28fH/75uH8/COWBYzZ2Hnzp2Mj4/XHu9///tn3ec3v/lNjDEsW7aMWCzGddddx+tf/3rkHA3IDhWRsXCEEYyMM/GdG5BCN3gOrOsr5fkoUZ9M/YouAIBvmnUMpABPmhZehUYE39mR5NeD3Tw2mmHzeJrfDrdz51Ank75bWyamwBVWI8AAffEAJQV7Cg6DRUVJC5KORs+RxFMzbDSMlBXZSvlmUfts8vcAsDmfm7fvRD40TAXgSkG3Z3hGZ8CyhOZd/evpcprdeNULfFUsTbtK1c7Gudk4H127hGd1Vifm6QO2vTA8AdtKYy2NGINhjz9W70eBIBfMnchkO2haD8RAQVDSgnxgeHg0YOujQy2PPSKiilKS85+3nu/f93GW9nfaZlFVGXRjMEaQxCMhYighSEmPtPQINTxZHmKnHqKNDCGaKVp3Of3vb9xKPjez7X3EUyebzTY9pocgqqxdu5Zf/OIXTE1NsXPnTu6880583+eEE05g6dKlAAwODjatMzg4WHtvsYhuYY4whq77t0rooXnCrCoFZmIlxopxqmV+gZZ4qp66aBsvCfKBIjCCpDK2Y10ly346xoBvYFdB0qMEA4VEy/G5whAaQdbVSKnY13AzMlxyaHMCUo5hT1ERVxpP2nWUsO72yUCSD+3YT8r43D0SY7tfb5i0qZBnT6lIrxeb1WgwxvDTkSH26QxLVDtToaCk7d18lxfn2lVnc8fEPu6YHGIiCFgWi/OCrm4uakuRDxXDZYlE4UlByglIuWViyrArH6MQVkMUtuTRakMYhJnfplbT7G6NVXNsj800GkbLsK9IrTVxWcO+gkZLyY+/egenXbxm3v1FRFRJJGMksh7+Ph9jDNpAPgybqncKofXGxZSVJF8qOynpMp5w5vQoNGKM4dc/e4jLXnrOITySI5+nU5QplUqRSqUYHR3lxhtv5NOf/jRr1qxh6dKl3HzzzZx55pmAzYW44447ePvb335Q45xOZCwcQQQj4+TvfJC5PAFCgCMMjtQElQZPGuvCdmVIKVRM+A6jZcmjk2W25QNCA70xxWlZl5hyZrj/hYByCBOBv6AYeQi0e4ZURXJx+ipjvkKIkNDAVNA6E7s7HnJRT4G7djTf1Xxhzw7evXwNbY5jTSIhCI1BCcG9kxP8dHQ/glG64lmUEOzOS9ZmrN8/LhXPbu/jkrY+CtpWF7S7ZYbLkrJWOJWqg8AYxn2XqcBhSbxER9bnsckkoVGVfdpqhXv3Q8p0M8qOOY9DACcm6g2fqt6FgSKMlA1Z1zauCrTV+fe1/czyIbUfd19bw2Trg3vn/QwiIhrZvWOYJx/fQ1YkyMokU5UGbwbDBOMMM0SZEo5x6Qja6aSLMiESrxJeW1jo72Mf+ArnPusk2tuPX+0PrUVF2+bgtnEg3HjjjRhjOPnkk9myZQvvec97WL9+Pb//+7+PEIJrrrmGj33sY6xbt441a9Zw7bXX0t/fz5VXXnlQ45xOZCwcQZQ2b5/ze6sNBKEi0BJXapQwBNo2egqNIKECxn2XbXn4wd5Jyg3bGS6HPDRZ5qLOOKtTMagYGEJY+edCCAnhog2zlj4KDBknpM0NWJkw3D+WnFMdTgjBaFnR7YUMlafrQFQHZXs9DBQ8hoo+/rSNDfs+H93+BBdmO3hGNktSKgbLZX45PspDuanKVjT7wjH6nY45EgptGOGxSQ9fuzgCOmMhvfGwkqhpxxUaGCxYT02bA2UTUgwlGsHDo7C3AP1yBbv1zlnLMgWghOTM1Ir6ng21BFBfC/ZP894aU/UC2cqRqXJd5z+WiMrUIg6MUsFWO0yYAvkggIog01aeYILxhiWLTDHJXjNADyvpJIUUgpipazaYppwjUfm/AAPbd+7gT976Sb7x3U8cluOKsFTzGXbt2kVnZyevetWr+PjHP47r2t+K9773veRyOd72trcxNjbGxRdfzA033DCjguJgiYyFIwR/6w4mPveVWd8LtaBU00OwJYoS8KQmNLYSQhvDSEnyg4Fck6EA9a/+bSNF0o6iO+YSGJjyrfiPBJbFM0z41mvQiMTQnyjV5JonA8l8Mk62DbMmJm1ny24vpD8RkHFsqedUIBguOewuSO7b75AiywjNZURFbfjZ2Ag/GxuZfR8I8sbOwt4sUYKql8Q2bRL4BgaLiuGS4uSMT9IxtZ9C62uoCEcBnqMZ9QXbc3aJhEhymjqTR8L7CafJPjlC8aquc8iq5vDN5ByS/NXunrqy34lSQDGsZJZIwQUvO3X2FSMi5qB/ZTfxhIdfCAgrYl972T3NUGhEEMMlLmzb65hwiRmXIiX0LLcB0igEgsD43HLTXWx+fDvrTl51aA/qCOWpiCrNto0D4aqrruKqq66a830hBB/5yEf4yEc+cnADm4fIWDgC0Lk8Yx/9O2S5iBBOU0zLGJoMhSpV9780MFl2GS+7PDgeVCbHuXlgvMi6tIusiCtBVRhIMBUKTFnT5tbFgrpjPjFZ13PQC4q3GbbmHYpasjrpsy4T1Ds0AillSKd8FJLfIuignb0MENC6k17zHgy2tZVgWXLmQdu7e5sbUceGM7ZMOZze5oOYGfCxMtaQrRgTVZOiU3bzux2XsqW0i10VUaZVsU42JpeTVF5tnwAjZatsWR1pdS/VTPVcYCiEIaWw/qshpSCe9nj+m85d8DmIiACbs/DK372EH3z5NzZkR8gwcyfKCiQp7F1n1ahOEiM/R5KjJqRUeU9Kwa233HMcGwvHbyOpyFg4AijeejtmagphIJEqk5+qZ8WGunrbPPsFNlr2mAxchIBNU/NPtgOlkNVJWzboYN3gCk1YSdDLhZJcaIhLcKXmhFSzbHRCVXUfWl3wguXJkMEirMvYMTVuo/r3ipRmeVKyKy850ZzAFp7EwUEgKFKqKdPNxRLVxspKh8sZIxCwrzRbzoSgrAUTvpjhRWlc1xGwKgVbG9pJKONybuoEnpGu+iTseawmkhVDm5NgpZxtKWqooVojWQ4NuUDXPAmNpDsS/OV//C4dUS+IiKfAO9//Cn5z/SOEgyEFkZ+nL4TBEfa7UTVgR+b0QlhcYlg/nsCfHjeMOC6ISiePAEr33F+LFcTjPvFkVXHNtLyTDw2Mlm3cSgjIB/P7t6pLVKd8TxicGYkKgqK2eQXT34opQ1rVhaJm24MnNR2ObQIVtPjN0gY2tmsMhpiIsYH1nCTWsU6cyCqWtzYUZDvrUy4b22YaSMbAWFkw6s91eRtyYetL3xjojDUf43CxWZ3SUO+NMVaGkZI1FIwx+Np2x5wMDPtLmv0lzWRgww+uFChhpbGlEFzwkg186f7/j3VnL285poiIuci2pbjmb17JPrMbY1q7F6s9I6pXd45Ca+NCgBQKiYPWmjPPPnmRRn30UfUsHOzjaCQyFo4E/PqEpwNFOlOmoztHIuWj1Nxf4pzv0BSaWMDH6TaEH+JKc15ngYACoZ7ZCtfMMVn3JYJK74OZ2gSl0LaP/sWwx/1jMW4ciPPgmDtreEQK23mxllLVMBFnRRvLWVYTQ2pUkVjutHFJZhlnd4YoSdO4jYG9RcXmKZdW3o95lSeEIaaqGQ2WsbJhoqxnNqMStllUmwsTZUPOtwaEgFnVKG1vDYEjJY4UbHtgb5TYGHHQnHTWcnbpJ3FwES0mpICAkimhK9fmQsN/jnQ48aSVnH/hxkUZ79GINmJRHkcjURjiCMA9cQ3+Y5tBa8JAIB2BUoZ0poxyDPuGZndNl0LZlAuQFA550/qLn5R1kSUwSGk4r0Nw35gmH6qm7U36kkDbsr9GYtKwKukz5iuKoaAYCsZ9WwpYTSisEhjB1pxisCi5pKeENy0yMNhC56VDtJM1GTLJCZRTJiYUp6ba6HbijPuGySBEG4WGmlhVoGGgqOb0e1SPPeuG84Q5BB2eFX7ydd0semJCsywl6U7UTTPlSDIyYFsJUo7AbegNkTSSnG+YauH1GR+aajnaiIiFsLSvh7aeNA8P3U276mWS2bqpAgb2M0wf/fa7M0+zqSqJVIwvff2Dx7UMeaNc88Fs42gk8iwcAcSf/+yGFFmBX1SEvkRrMFKjlFU6nI3G722b55Bg7jtUF0WPWy2TMhVJaHvvvDzh23p/E7KlOMSD+T1sLu5na042N42qij6FirgyLG+bYjQwlDWEpppQ2PxlMAjyoeDRyeaxaQMPjKqWd/lKSJyggxe09/Pstl563DgG26yprJ2K81QQGkloJEJIOr2ZolaNo0k7mrSjW4Y5QmPbRj93KZyQrneRdCR09Wd46w1/zBu/eTVv/MabOP3UNrIqJETMMKykEKRdQdade19tUWfJiEVAKcU7/uSNlEWRwXAbxoR1pTaqqo6m0h8lRgkb7oyTaPldMBgy7Ql+cvsXOGn98ZnYGBF5Fo4InKVLyLz1d5n88jeoJg8OjqbYPZ6m4Ls4QtObLFQm92pyoSHlBIyV7eQvBLR5itGyg9SCMiEBIQbbXc7DISkVGa9eWbE0bmOXUkBnTPPIvr3cV9hF2NDM+vYpyUu6+rhySTvGwLjvMBm49GbzZJJ5frGth4wjwKkaEjZ+Pz2HzyDYkVecmvVxZN02Gvfnk4SxUsi+sV6AciAYLlt9CCkMPTHomqaS2u4aSloz7ktExX9QPW9JpTmjvYgrwDfGSmZXBlBtE608RSAdnLxP1oOzu+CMLpus2HFiD8/78puJd6RgdScAj/ghuwtWGW+2uy4hBClXkA9CpjsYpBQ8541ntzwDEREL5e3vfD133/kgP/y/WyjqcSQejowhjAI0nknSQR8x4ZEWVnNFA120M8zozA1Wrum//sLb2bNjP3//V//JYw9vJ5WKc/nLz+eVb7iU9o70YT7Kp4+no3TySCEyFo4QEs9/NsGO7RRu/DkD4wm2j7ZTnUYDI9mTS5J0fDJegCdDHKnJeAGFySQJZS/AlCPoS8QYKpZRWkKDlyGpJN1xj6RjJ86EMjVjAeAXY/u5p7Cn9rx6PQdort+/m2KoeM3SNGO+Q3uiRDxe5NZtS5oKI4SAmISeOAwVZxoM2ggKoSDdEDJIObaUUBurmNiY2iAr3TNjjk3E3J6XjJSbSyF3FgRPTGlOzWpSlbt3IaA3pnGEQQprMO0vKTKOZkPWB+w48oENYbS7AQjbfNqVGteUyWoYU66VgLa2EEvPX8OzrvsdHK/ZQ5Jdt5TBe8eZ25thz2jCEUz6DeWSStK5NMPlbzm/xXoREQvHcRz+5Ruf5Hv/ezP/8pXvcv9vHycsljAIAmETaRSKdpFECVkzbpfQjmskg2a0KdlRIAhkia989nvc85vHUUraDpQCHrj3Cf75uv/j69/7AOvWr5hrSMcUmoPPOZhNdv9oIApDHEGkXvtKdDLB9tG2yiuNmfeCXOAxkE9Q0Ip0ssTXt0t+OFCipG11Q5trY+x9yRjLkjG6Yy7dMZflyRi9iRiOECQcyDiaczpKNdd6KdRcPzw4YzyN/HpyD7lKB8tVXePcsbPLjnDadS+Evaiyc0RDpLAacRobtlibBkdY+eXpOZDaGEJjWJcxjPow6tsft8YH2GO+b0w26RYAJJTB14K+RJGhks2dGC57jJQ9JgIX3wh6YmXSbkjGDUi7oe2cWflWtLs+nqxLUA3fvZV9tz8x45jSZ6xZwA+AID4tNHzqRav5qx++hUxncp51IyIWjlKKV776Bfzgxq+wc9+t/Pf3vkAobSWPT5H97CApvBlesA6R5SSxkhWilz7RjTRWDVLpGL/9zWYUDoS2t4o0EqMNY6NTvO2qT+H7C9dIiTg6iTwLRxAynWbIWwOzuQNrCIbyCX6xL+T2IQmE/GBviWd1x8i4mq64ZH9R4ElZa1Rk14LuuKEvEbAmGTRN8o8Xpijq1rXTE2HA9tIUZ2fLTPiqqUnNjBEKSCgYozHEYMi6hrgSFEPYMuUyVLKiSidkrcGyrxAw7jebDAaY8g2F0KnJI8/cnyDpwBOTsKG9/nrV5X/viJ2ME0ow7gu6KvoKSmjizty5IMZAxgnYX1a1sWz5zh30P3s9AHse3M2PPvlTHr17N8XABjscacM6s42zqz3O73z8ZYRByJoz+uk7oWvukxgRsUgk2pstd8Xs1ydYb16GJMYYJoTHKDlcqo3rLKKS5yRR6CBk7+79/OT7d/LiV1146A7iCOF4FmU6YM/Crbfeyktf+lL6+/sRQnD99dfX3vN9nz//8z9n48aNpFIp+vv7edOb3sSePXvm3mBEE4Wx8rzLhAZu3Fvvu7CzEPJvO/P8en8eT8LShKHNsxN2QkG7B31JQ0LB8kTdUKgrDs4j+1ghF4ZIAVNld9YeEo0I0dhnwsYqTs4EhGh+O143FKp4UrAi7dEZm5mZvSNvJabny8IeKjX8oAmY9AV3j/iMlCXdMUF33EpNF0J7DlNO2DJ+KITVlaiWTwpg5DF7Ld/y8R/xiRd9hbtv3UYuH9S8JWVd11qYjhf4nP/SDVz4io2RoRBx2OhftqTpuxPO2dmlmRCNQyUnaprnrPpcViop/v7D/2nDE8c4ZhHKJo8bYyGXy3HGGWfw+c9/fsZ7+Xyee++9l2uvvZZ7772X//mf/+Hxxx/nZS972aIM9njASc3f/OOJqWBGohxYo+Ge0RwISDuGzpihMwZp17aIPrNzinavXEvSKWvbo8ERrdtSV+n2PMpa4anWkyzY7ds6bmsSbGwrc3bfGBPCpxg0GwpQv9PpSzioad+lufQeplONQthETOiJlWn3BCuTmlRFr0IIwVBZUtRiXoNnNoSSbPrfe/nfL91Z+QxmbkSbmfkacQnyaM1sijiqWbKkk5PXr649n2KSkinNatA2MsEkEjVnpYSo/IeBgd0j/PInDyzmsCOOMA44DHHFFVdwxRVXzPpeW1sbP/3pT5te+8d//EfOO+88duzYwcqVK5/aKI8jlr/mQnY//IMWSxg2T819ZzBcDrhl3wTLEx7Lkx5JJYlLQ38iZKIcpyMzyc6J5vKBFV6GDhVjLCzNWpkggD4vxqpYHKMNPW7QsoLBGENJ2zqEUzIBy5OaVV2T9Lbn+I/N3fNO/u2eYn+pfozlUM8ZgqiNUYiKCJL1HqSUJqYcLukBsCVjk4FhpKLqKIGStmGcVoSmHkoxBpZcsI6b/+5myvPUSvvaaj8IIUgqyDgQiihFKOLw4/sB+waaQ5s79DbWqdmVGI0xDDHcUjC6GYERmv/86s+49IozD2aoRzxRGOIQMj4+jhCC9vb2Wd8vlUpMTEw0PY41Slu2M/zRzzHwR3/B4J99lInrf4KezM26bM8V55NuU8xeUFhvRNQK3xi25kvkA0NoBB2uQVUm2j1TsyXTCV7Ysary1/R3QCJ43ZLlTPgOT+Ti3LmnmxM7p+YcoxAwUfbZMlliMrBKkcs6psj7kkC3vuQMNiTRiBKQcmYqJ9bWMYZAG7IeDJYEba4mpmaqLGYcQ7cX0u7an8HxstNYhj7LdiEXWJVMU6n6WPmC09g3mGtSdpyLrAu9cfuvENCzfum860RELDYP3Pc4Y6MTTR6CfWaQHXp7TXtBG4OuyESPMso2s42ZKcezYzD4psQtv7iDh+6bmQB8LKEX6XE0ckiNhWKxyJ//+Z/z+te/nmw2O+syn/zkJ2lra6s9Vqw4dkpwjNYMf+SzDP9/H6N87wPowSHCbTuY/NfvsvcP3kd587YZ6wghOOPjb8QRtftZGiflhApnZNXPRbsrUcKQqiXxWSGj6ROdBlbHs7yhZz1L3WaBoH4vzR/1n0inyjJY8ihpO+GPTiVZkS0gRTXUYLfpSUOXq1mflVzR5yKQ+CpASUOsZU+J6ghnuvBP74DT2wKrljBdkrryfLwU0hmTZB1TuahnS4SEtAMxYf/WCPYWmjtGVv+uhmkmfavDoIET/+j5xA+gpjxWSXY02CZTF7312E8AizjyKBasTKrCoZ81bOSZnC4uoF338aTezm6zh/3sZ5BBHtQPsdlswWAIaO1BBGsoaAIQEOqQN7/mY+SmCof+oCIOO4esGsL3fa666iqMMXzhC1+Yc7n3v//9vPvd7649n5iYOGYMhpG//TKlex+aUV4IQLnI0Af/jr6v/g0y2Zyn0PGMdXSt7WRq+zBBaKdhJQyOsHftG7KCx6daf417Y/Z2dnm8TIfnA7BtymF33kGIkJhSdMegPqkKVsYy/F7vBkaCIrnQJ61cOpwYjjBMBM3dL0MjmSwkyDoaITUYUdGjtzOxKyCuBMYY4pWrLOYYTmjPs3UsOWcoQgjBeDmsySity8C5nXbSfWZXmbtHHHxjSyiNMRQCQynUrMkqXGnIOGb2813BGHCkYSoQlLT1HPha0B3zSTkaIWwVxVjZYbhs24UHnsfFf/FiNr7yTMqTRdoTgr3z/B6qBiMhNHD2q85kwwtPab1SRMQhYN3Jq3CEy1pzGjEStXCeEIJu08cQe9lj9mBE/TdFGoeU6UAIiRHN98I2U0FVqiIMAklgymgRMDoyyff/65e8/vdecFiP8XBxPIchDomxUDUUtm/fzs9+9rM5vQoAsViMWCw25/tHK8G+/ZRuv2fWiatalkehSP7nvyH9okunvS846T2v5IE//QpK6Gk344aNbYKfDxnG/dkFPgSwIevx/KXjpBzN/pLkf7YnGSkrJAaDxGCT7s7u1HjTNIo7nRidToyqYRCYugLiTCQ6FPhG1KSXq+N0BaQU5Er2MtMGYo5fKbucmYNgjGEq0GRdQ7tryyCXJerJj0kHntUTsL8MP94t0cbKK5/WofCUwJN6QUmLvobNU/Vj3pkX9MYVnZ41YVQqxinveh49pYCO5R2c9LyTcCrKl14mzjmvOJ0nv3Y/pWl9MBpJOeAbazQ9788u5cK3P+u41tSPePpY0tvF+WvOZ3LrTIlzKSRLTD9d9DJlJtBoJA6g8PDwTZlRsb9W9SBRSBSmViEkkChiJCmbIlr6/PoXDxyzxoIVjztIUaajNM950Y2FqqGwefNmbrnlFrq6js8SscIv75xzeoW6wVC4+4EmYyGcylN65AncMOS0j76erd/4OVOP7a69r4QhEw+45mTD5zcL9pWoTP2i0lAJzmpLcUXfFDFlu0D+x9YUucCOpNG4KGq4awTO7TLElKAjXuKk7nH6M3mkhImSyxP7M2wdnb2RFVTc9aaxuLB2hPjGMBEIRCHGZNHlx1sz3LY7S0wZ2j1h774b/P+hAYHihAyc0xHUzlPjvgAeGJX2OCpegLQT0uFZEaZSKFp6FoAZBkVgqMg1a7rjgr7Tl/OMN82tqnjuNS/g8Zsf57fbiwQNPT2qBlWnB8uTtvfGKVdfxEXvuKT1gCIiDiGlgo8/4CDwZ31fCIGDQ7vpwCek3NCF0rF9atGESJya0dBodFT/dolR0gFhq770RzmRZ+EAmJqaYsuWLbXnW7du5b777qOzs5O+vj5e/epXc++99/KDH/yAMAwZGBgAoLOzE8/z5trsMYe/aXMljt8qgx8oWV0F4/uMfPV/mLzhV/WW1UKg3B4cZXAJcKXBqSTudcfg2lMND48bHpkQbMu5pJRDbzzGiekS8cpyv96XZCoQc44j0LAnDxf15zh/xRBQn0wzns+ZfSMszRT41faeWUMHNgthTpMIjaGoBbc82c1te6wHqRTCYMEQV6bWJ6IYGmJK4kjDYFFxzyhsyAYkG67QQggPjzvszE83zU1tuRBQZqayZOM5n3lnYJ8PFgUdnmHti8+Y43gs8Y4UL/rs6wh+718ZLsFIybaiTjjQEzNkHLsfIeD0q85pua2IiEPNvl1jlAqzGwqNuEJSMv4MQyBtskyKcVTFo9Cq6ZSDy8o1USLvscgBGwt33303z3nOc2rPq/kGV199NR/+8If5/ve/D8CZZ57ZtN4tt9zCpZde+tRHehRRfuBB/HvvY778UWPAO2UtpbvuYfKr/0Y4MkZMCfxQobUkCCA/nkcAcVfPmAClgI3tsDIpuXmwnnjXn7AJTSNljy2TrT9ig2CoaDhv+TCC5km2+vfSdIETO6fYPDIznBRWQgqtjKKSFmwbi1Fv6mQphjRVLhYCQ8az52ywaNtad3iGmLSlmCOlagvsaZUOUGutXUsHndVgMGgjZtWoqG7Vb89ywuWnznksVfrOWc3S0/txH93L0sTMDQol6Dt3DdkVnfNuKyLiUBJPLuwmLcRUWs81kzBJAnwC0bpTazWX4Ztf/SEve82z2LBxzVMe85GKDUMc/DaORg7YWLj00ktbinnMJ/RxPJD78r+glCYM5jYWbCmeQN9/F7kffw9hwHEAB2LxgGLBITeZxGoEzL2NcV+xK2+TEHUla9+RdoX9RQ+/RVy9hjBI0ToxcF3XBJtH0kw3gGaGH2ZsvCLOBG2eVU8szSptYJcLtWmQSxaMVhpHVa+rQtC8sietATIRQNYxeNKQCySeNNMubkNKaVJOSEkLhkpeTca5NgIhWPGC01Dewr4Wz/7YK/jB1f9CeaqEaVCvE0oQ70hx8YdeuqDtREQcSrr7s5xwWh9bHxnAHMBMFRqfPJOAIW7S5GS+5VfdVP4rFcq84+pPccs9Xzjm8nSO5zBEpBKzyARDw+j9owghkErXyvAaHza5zxBrC5FDO5GORir7RdsxnuDO3R08Np5iMoRNk3HuHEkzFcgmo6EQCm4fznL7cBs78zEcATFly/UmfEUxlARGknFpqQkgMPQmW7sohYB0LCAuqxNivZxT1J7XScV9lnXlWdJWsAYA0J2QrMpI1rdL1mYF3ixXnpIw4esmy7taBw4w5esZXoFLn5WloB2GSg7b8g4jZUVoBMVQotC0uT7trk9PzCfthpXOmIZndpXo9GZaLe3L21uei6Zl1/Rw5X/+IetffQ5O3Orvu0mPDa8/n5f/+9vILOtY8LYiIg4VQgje+OfPa2koKAROZWLXJmSveYLN3MUuHmUXj7GFu/FNYd6bQU2AQLJr2z7uuO3hRT2OiKeXqJHUImKMYepfvw0IjAYdVmfE6W56QzwTkGjXmBD8ScPOyQQ3bekj1LIilQwaWy7pSdg6Fee0trwt7dNw53CWQlgvZ7Tfc0NCGiZ9l954ABg2tofsKczRAhIbhnjGkuICjg06PU1JB5S0oBgKQmNoczSjgd1+JuHzrNP2sWpJHoAf37MUMwbTb0dSDpzYJtg0ZmqTv8CwIgk78jDm64owU2hzIgyE2uY/VIq+QMDL//hiOoMSI4/dhwlN5bzZYIcShs5YMGt1RDW59JSsz23Dde+CkJKzXjJ/CKKRdF87F/7Fi7ngfVcQFAOchHvM3U1FHP1c9JJTecenX8aX3v8DtDFNhoNCkJCV74Ex7OIxcozN2MaY2UO3WDurmqppuGFQwkWbkPf80XX88qEvH5LjebrQiINuMR21qI6gfP9D+HffhzGaclk1eAKaDQVjBFJVkuAUDJXi3LipnyCs36c70rAsHrImFbIsERKTii2TKcbLDrvzMfJhc38FRxiWxQN64hpjFAP5ODGpWZnSrM+GtX03jgMMyxOaHq91BYE2MFaIYRAVtz8ERtLmatpcQ1xqUnGfV128k5U91qAZGI3zxN4Ms/kthRA4ApalRKUPhO1dsTplFQ/BdqGcCEKGy0V6E4YzOwWrU5K+hGR1WnDpxk4ue9VpTOwex4QGhU0sdEW1C2Xrr6QU1vhJqnr44Hlvv5BMd6rFWnMjpMRNzmz7GxFxpPCyt17ANx9+H7/3gRegsAmNSalISFVr+R7IyVkNBYCAEpNmH1APOdSNBENAqfZ1V8JhcO8YoyOTh/y4DiezeYqfyuNoJDIWFpHcl7+GkLohrjXbxGFfK5cUE1Mu+0bi3Pjo0kqCnn3PEYYViYDENMlibSQDhQTbc7FpW7QyxrlQUgoFoYH9ZRevEjZ41pKAC3t80g1+pKSC87pCLuvz2TqWIVeeu+20AB4fyjLuKwaKDrlAoYQhoQxSwtJ4wIUn7yfuhlS7Yj+2K4MQc30rbBVET9ywJgOdnuD8Hkg44FbWF0KwLOFxSU+aFUmXjCNZk5Gc3CZZnZaYfeNcf/XXUEogFLVqCFH7sVrYNzJTkX5e98yVXPH/njPP0hERRzedvRmuuubZ5J1xYkKihGwycEf1QMv1y+SYZIgyeUICNCEBZXxKNVGnxo6Un/zA1w/dwUQcVqIwxCIR7t2LHh1BKSj5ktYVAoJSTnH/E0sIDRTKTtMXtssLZ1Qm0PC8rOvtqQGyrnXQ93ghjfpKBklo7GuntWtObSszFdiRpR17d60rOgk/39rLJav3kYkFaFNXDQC4c3cXtw+5dLqGhLI7UKIe73eU4YT+yZqhAJArOrNa0BIrP60qoYBUEvoT9WObqpd4k1DWaChqQyAgJerLCSAsBZSHJpDaoKZdyeECk4hWJQPGyw4v+YsXRF6BiOMCIQQbLl/Fnh+Po4xquu6LlGYsX81TEEIQmBKeSOFTrPwEiTkrJIwx3Pyjew7FITxtVNtMH+w2jkYiY2GRyH/968hKFQILuBiqAYdwmgdCYUiqmZUJWht25GFX3lAIfTKupDuukAIS0tSaIzVigEKoSKBxpN1mpiF9wRjwtWAqVBAqfrB5OSuyOZZn80hhGCt6bBnJkPMVY0GeG4f3k3UcBHBy2uGy7hQCgeuEqGn9KtKJoK5UWUFi5Zhr50DU/zVGUNCGoWJ1WWiPVU+CIDCGsrZJnI2MbdlHx7I2gsHxptenAsWSWKvETRvE6Y0HmKTHio19LZaNiDi2+OP3X8VrbvhzVptVJEjUDAKPaq8UgzYBofGpJzMrlHTxTQFPpGhlKIA1LqYmC5RKZWKxY0NjxyxCzsJ8XXePVKIwxCIRbHmiNucLOZ8L3FAKrXfAqcTsqygx06MwWID/2KG5aTDksUnN9nzIQ+M+t+0rYrRPtmIozPBEVAZUCuWccbKRhvJBbQTbx9PctnMJv9zRy4P7OigEdalmgPEgYCwI2FsKkEIQaBgtOjNCGOuXT8woEap2gpxLAhsEaceexlVpWeuUWaU0hzDcyS+Y2XPBIBo07WeKOIHNC3ElnPmctZFXIeK44pRT1/C5f38vm+QWHgs3scvsZofZSVnqShfXEqEp0/jdMYQEukhZT9GyXWtteRugf8mz/+TQHkzEYSEyFhaLIKyVKDrurEICNQyCoVwCsKGArFPvxjh9zdES3DgQkp/eihHwDdw6FDLlmwb3vCHtlsl6JeIqgIolXJ02fW3YV9QMF+2dei6Yqx22pRTCSEmQEkme09HP+dke+r0kOwoB+0owXHaYKDvsGEyhGybz3vYSJ/VPNGzb4M1iCDWijWFVGtZlJVlv+oKiVhkxnTHhIN2q0WNw0HS4PsbIWctGBeBKq+cgBWw4NRJOijj+eM7zz+XeJ7/N6//gcnwZEhrDcr0WxziYGb9Edcq6QNK0A7J1KaUBjWbz47sIw9a/iUcLx3OCYxSGWASMMQhHYirxdinB9UL8skNz7oK9SnJll+F8orZ+TywgF3hWQc0ISiF40k6s94xAuUV9dGjgvlHDs3uhK15kSapQE2UCKPiK3VNpCgHctj/g7lFdu0NPO4JTMpK48Gq5AhkXOmPgSEEugNFyPV4JkJQOJyfbWBFPEmqXsob9JfjPO3o4Z53DeWsmySas2uRlZw2STgT89okOexbm04ZC0O7BZNBcPRKT4Ah7p1L1QFTP+4Rv2LEvx1lvOI8HvnEbnU5ItmKsGQNBLdnKGgdKVEWf6nvoXtHeemAREcco6UySv/ib3+elr7iUd7/oixhMJfTQCkPO7CdNj01snKuUUoA2PgLJ/fds4uzzjv6uq8dzzkLkWVgE/J/9BDktMcj1NLG4X89jwE5QvhBs3t9BY56CK2FVqkxKWQ/DmG/vkqd8GCjOb5E/PmnoSRTpz+SbDAWAuBNyQvs4P9lX4tf7dZMrfyow3DVa5MGJIhM+TPiwOw8Pj8JE2dQMhcaxVkusUspl65ThtyOwIwfbxh3+6+5u3v9fa/jpw+0YY+/aL1i/n1NXjzJaMguSOW1cxpOGnpihwzOkHVsaaQ2HulDT45MhbszhvD+9jKU9cdq8sNaXoaoCab0SktDYDprTh5Hqb59/YBERxzAbL1zDn332lWgRUmae/utA2eSQKMKGplPNpZQQGL/y3DA5mT8Uwz7sGMSiPI5GIs/CQWKCgPIPv4eUoB1q3gUA5RiUE9RddQIeeLKf2aokPAnLEwGTgWBrzkUJBWj8BcywMaVZkpr9y1hNMnxOX5n7xmZvBT4WlK32gxB4QpJSDgMFQdK1g5ZUGzA1Byz8Bgu5mgIVGvjve7rZNZxkdVvA9n0pimWHpANjZU2H17rJU7FizDjC0OHOTIYEUNgc0t8MB+wrap7x/JPZ+esteFMT88jRCowx+NrqRQgBTnuK7MbVc68UEXGc8KKrz2fDM1dyxjk3zrusqNxnSgQ+RZRxKzlSAk1QMSLqRv3tP3+AZz8vaqp2NBN5Fg4SvfUJmLLCI8o1SLeef1CbQgUozyAdw/Lu2UVKqvbEvqKDFLA0UWS8LBeUePeMbn9eAaIzOzQZd27DYyIsMxH4DPsldhZzIAyOgKwDHR60u9DpQcahIqRUFVSanXt3JHlsZ5Zi2eZEpB2DFNaYmCtmlw+gVBGmSjutkyFdBWd1Oly8JsPU3U9y0598u+V4KmtStSaqNtiqt16OcNTcq0REHEesPrmPSy45D6VaTw0J2Q5Uf+FMpbF1iYAiIQ0VFEIghOQLn/8Pdu/cdyiHflioNpI62MfRSGQsHCSmXA8/CAHKBSdhUJ5GugYV0zgJg3Tseyt6JhCiblBUJ87AwBM5Fyk1z+4doy9eZn22hCvm/4h6E4b5rj8hoL2FsdC4BV35f9ahUq1RxxXQVjMY5sY3sDNnGC/bJlVxaci62oYQaDYYtIFJXzBRyVWYKmtisnWOgzEQV5ANfO75l9sXfCFXdxsaAeecytKXPXOBa0ZEHB9c8//eQhjOUXoEOMRIiHZg3hZ1NWI6w799/YcHP7inmWrOwsE+jkYiY+EgkX3LZrwmBDXjoCrrTCWOvmlvB3lfsaJvioQK8GRIKTT42nBKNs+5HTl0qChrSWcsoMMTuC1mTQn0J8J5v7S2Q2WLmuhpW1gSk9R7TjQfG0BKmTm6R9YZKhpcoVnihXTFQjo9Tbun6XBD4jJkrAwjZcG+kiAXisr+BCm3tfx04zGF2sZEy3P/tk07TqvpsL+suO1XgwtbKSLiOOJ5z7uIf/jsB5FSzvAwKGIscU+ueTwVM/vOGGMITRnf5GsP0PznN39yOIYfcYiIjIWDRHZ2IddvmHc5AahzziN/6kXEUgGuNMRkSNIJWRL3WZb0WRIv0xEv05Ms0ZMogQlwpYMrJJ6QMwwCVwjSSmGEaCGtbO/cHxqTTCzQWOj0FDElW+YWuFLQ4uYDsDkCj4xLtkw2b0gI6xVocw1l3SxKBRCahSVDBhWlSQn4WlIMRYuyJENjxsWYryhPFtl+13a7rXLAwz98iF9c93N+/ZXbGNm2f/4BREQco7z5LVfxwMM38u7/91aueNGluDJBQnXS7i7HEbGaJ9Ih1iRCZ4wmoIBuTHw0hgKTDA2Ocusv7jzsx7KYRAmOEQdF/E1/QP7a90CrWmIhiF31Rp6VbWf4nwbY9YNtSCGstLLQdMTLtURCsNPazkICRwq6Yg6j5RCnIZNfAJ4UdMQUE76DlEW0nnkRmkpS4v/ucJsknOccJoLUdDnGOZCisSy0cZ/NYZHHJwSr06ZJfVEIaHM1+0pyxpdHCMmEr8m6Ns9hNnw9s3vbQNFlZbIMZnoIw46mahmHBoZK9tK/+1t34efLXP+e/6U4XkQ6EqMNP/vbm9nwolN56Sdfhhufu2tnRMSxyooVfXzgg+8C4IWXvIPHHtlKoEtMMUyMFC5xMODg4puSLbucRS666oXQJuTtV3+Ch7ddfzgPY1FZjJyDKGfhOEZ29xD7w3e1DLI7z3k+sqMTqSRdq9tQrr3TlQKynm97JnT4dK4s0LmygJ+qy4J6SrIk7tDuOaRdRcZVdMUcOmMOUgiSTogQBikbkysthUDxyP52Ts+mSKqq5sC08SMRiEpPe0mwwKvZ3sWbaa/Z542bMMDu/MxzI4WtApm5XcNgEcraCjVN36c29UqM6lanwiI/H3uSL+3azPeHtjPulxv2Xj9mDTwyEav1jtj88038xx/+O8UJqzOtA11r3/voDY/wvfdcP89ZiIg49nnLH12JrnwvQnwmzRD7ws0MhpvYH25lKtyHb3K0vB0RMDGW44nHdx+eQUcsKpFnYZFwzzwH8afvpfS1L2HGx+o1i0rhXvZCvFdcVV+4o4eu3iK58TRSaFKpgN6Tcrhxg6m49tuWlll94hSPXL+SsZKtikg4gsS0/SphOLm9UNEVMAhhKpO49SPcP9wFSHrj8Dsrk2zPhwyVQgIDD42HVKdb2aDzPlLWBNrgyLmMH4MnDed0lrhnNFZRkBQ1j8J0W0Mwt1TzbHaJEIIw4XLncImTsrAkXvcwGGjKlTDGcMvYo/xi/DEr2oTAYPj3wYe5onM9r1qyhoQSFA0MlRR7Cg6GeoglKAYzB1DdtjY89pNHGXxskN71vXMuFxFxrPPq1z+fu37zMP/+zR9TDMcJTbMHISQgMAqXROsKLgGf+sDX+fJ3/+IQj/jQsBhhhCgMEYGz4TTUpz5L+PCD6H0DiEQC5/SzEOlM84J9a+haWmDXlhSuCli6Poeqlgo23Gl7XshrLx7gSzf306wEWedZfRPEpzVnst/VqvJBPXQhhWBNymFNSqEE9MbK3LzPzuKNGREa2JrzWZeZq/mLYE26iDGGtVm4d5iWGqYGiE/zIBhjPQf+LKutOGcF7//Gm3j8rp3c+M+/4dc/30KyXGJDm8KI5vDDryY2ccv4ow37MrV/fzjyKHHp8qz2E9g85SCxAlgH0gZCKskjP344MhYijmuEEHzqs9fws5//gie3NpdAShQxUjZ3YQHfrbvuuv8QjfLQczyHISJjYZERUuJsPAM4Y+5l/BJKGFaeOEUpL1HuzC6TYGWjT1pW4MWnjnPTo21Nd+eOMJzXO07czXH/qGBdxpBs+DSNgWKg6IyVEdgEwKlA1dzvWTfkgm5J1oVbh/SM5Mcd+QApYE2qnusgsCWTJ2SK9CV8CoFga07Q5hrGWyjECmBZsvkbIgRMBdXkxvp7K9Z18rovvwHlKDZcsJoNF6zGGEMxV2bbTY/wiw/9H7rSJ6OsA34+9tjcOwZuGHmU53WtJi5nJDIsDAGlyZlx2IiI442HH9rEk1u31Z5LFH3iRDrFMqRQlCmyQ7T+PhqjGZ7ce4hHGnEoiIyFp4NkGjR0LS1QLrZOG5ECLjhljMJwitGyoBACGB6YmOLzT5QJjU2+c6XhBX2a31kT1vIApkoe8YpSYUyGpJ2QyUDhCoOnDMYITm2TrE0JPrt55r635QJ25QN64w6etO2sr1xeoDdhTYeEYzAmZGVa8tDo3NHKk7N2f02nQIV0Zgx9gc9UoBBS0NMT55Jv/D5eullpUghBIh3jlCvPYtk5q7n1r3/Mtl9s5oniPspm7jACQE77bMoPo2SlBXVlGBKDK6BUC9nMjtGGjpUdLfcREXE88L3rb6qFGwWStfIcEmRrYQePOAmTpsDUrF8pYwy+KRIGxcM88sUj6g0RcXhZdgK0ZVAphVdputQK5dhlOmOGJXHNT4cneHDKr3kJwE7kP9ot+euHHQINU2WXiWI9jCCErV7oiZfoipfJej5tsTIZt0zaNfTGZ993YGB3IWBrLmCkFPLwuNP0O3BWZ4Gihg3tkJxWRCEFrE2HnJxtCJMgiEtNu+ejhCbtGJbGA9ZfsJKLv/ZOvM50y3ORXdHBFf9wFW0rOynR2lCoMhEExOMO7X1tuJ4k7RiyLiRd22uiVVKWkIKNLzt9QfuJiDiWmZrKIaWdMrrE8iZDoUyJfey0hkILtPFJJqdnXh09mEV6LJQwDLn22mtZs2YNiUSCtWvX8tGPfrSp26cxhg9+8IP09fWRSCS47LLL2Lx5lru/gyTyLBxmjDGYm76FkkXCUFAYUyTawjkNBq1hctIDbA3kwxMlBsuzl2gaBPePCm7Zk2BNPAbVZBxjUGgSlW6MjfuyGQ2aczoUP2rhHbR6BoLJaeGKlGN4Vk+O+0Zj9CYcNFAMwZMha9PQEasGMOrrlY1kf8mjyyvTceEpnPiul5Bc2TPPmaujPIcrv/omdrxhGPbdNe/yCZGGckhpeIKk0Q15HVbvwZZhTssJqURHLv/LF5LsTC54bBERxyrr1q1GV/rQd4nltdcn2M8AVq9EtlKcFQJPpnjlq597SMd5KDEcvGfhQBIcP/WpT/GFL3yBr3/965x66qncfffd/P7v/z5tbW38yZ/8CQCf/vSnue666/j617/OmjVruPbaa7n88st55JFHiMfnuAt8CkSehcOMefjX8MCtAOQGHSYHvJaeBSlh+440ElAS7p8sz70wdo77xT6wH219wzHHfslrOg4GRkouT06m2FNIsCTmEpezXw4C25paCUFSNctUGwNJx3BGR5llSehPGF6xcoJLe/0GQ2HmFn2jKGlJZkXnARkKVTJ9bbz/pg+xZsWyGeqTjeNe5rXT5XbgCI1nrNJl4/mWwrbl9hqSQgG61/bwqs++mnPecO4Bjy0i4ljk1a+5gljMeiu9StVDkXzNUBBC0Ep4XgBSOKw/6aTDMdxjgl//+te8/OUv58UvfjGrV6/m1a9+NS94wQu4804rbmWM4TOf+Qwf+MAHePnLX87pp5/ON77xDfbs2cP111+/qGOJjIXDjLn7J4AAKZgaVJSmJJODNu+gsaDAGPvYuSfJ0HCiNsFNBK1lEw0w6teXyQfw0Jji9iGX+0Zd8pX+C/tLHkPFWK2yQAjB+owiVVGMlIBCEBP2NSkEGsGpbSHDRZfhksuErxgtOzw5mWDzZBKEQEnBjqkkk75D69RoQ1ErgrHWbstWSCX53Of/CiXEDINBInCE4vVLzmRpLKQ3pul0NWmlicuQmDQo6loXKdf2vOhb1cY7fvJO/vAHf8QpL5xfmTMi4ngh25bhH6671nagrYQAR6lIph/AzfbffuKbbN96dCY56kV6AExMTDQ9SqWZidQXXnghN998M5s2bQLg/vvv51e/+hVXXHEFAFu3bmVgYIDLLrustk5bWxvnn38+t99++6IeexSGOIyYMIDhXbZ+D9A+gGR0Z4xyQZLtLeMmrMUwPOnwkwcztE+lm1QMk0qQD1tb7xlHYgzcPaK4f1TVXtfAzwdiPLOnTIfrzvBonJSFHTmBMgqDmrZdwwnpkCVxw6MTMcIWdmYucPC8eRpHVNx5MnZw6oiZ3Zq3L7uUG/c/zKZCvdfDycleXtlzCv1eW+01KwJl0EBJa1wpcSt9JTQCKWHJ2m46V3Ud1JgiIo5VXvv6l9Dd08kn3vktwgGHnKi3ha/qnMxHuRTwsgvezf/95h9YuXrpIR7x4mKMwBxsGKKy/ooVK5pe/9CHPsSHP/zhptfe9773MTExwfr161FKEYYhH//4x3njG98IwMDAAAC9vc2l3b29vbX3FovIWDiMmPEhcCqTrBC4aSjnDNsmHfbsi+E+qlnf5fPFx9sRvmJVGjrbmw2DM7IePxsuzunsM8A57Q73jSruG3WaXgdrMPx6KMZpbZrV03IJU47g0l7DnfuplELaMILAsCJp2NCmeXA8iWjZCVOgseJQdvW5hZ0cYeh81mkttjU/Q4/sYXm8kzf3XcxUWGQyKBETcVYnZ7+0hQAFqEq7bLAqkmVt0EKQ6mmdYBkRcbzzvMsuZOPNG3j9GZ+AWe4JmowGAxk6aKcHjzgGzaQZY6o4zjuv+Azfe/gTtaTJ442dO3eSzWZrz2Ox2IxlvvOd7/Dtb3+bf/u3f+PUU0/lvvvu45prrqG/v5+rr776cA43MhYOKw/eCsI2O5ocUDyx3+Nr9ycYLTt4UiCF4NYBw9mdgqQj8KTGTJMHOCMb457xMuO+nmEwCKAvLlmfdviPba37O2yaFKxM2bvtRto8wWVLDaNlyAUhWyYV475kb1Gwd6+DErA8aVidAjmrwqN16ysMYcsyD0GmO077+etbjnM+lFtt62lIqzhpFccV4Yzz1jRCY3UqbDWJXdcVgpIxrH/ZmQc1noiI44FS0ceEhjgp8mamd8FgEEbQxxoytDcYEIp2ummjk8G9Y/zo23fwkt+94Gk9lgOhMYxwMNsAyGazTcbCbLznPe/hfe97H6973esA2LhxI9u3b+eTn/wkV199NUuXWs/M4OAgfX19tfUGBwc588wzD3KkzRyfJt3ThNn6AKWc4PGbk9z9iySf+U2KQuiRcR08KelLCC7okSQqPRxmm/DiSvC7y9OsTMy0805JK966KsFAQRHM4yora8HIHLmSQggSCh4Zd5kMZJN8a2hge86wadIQagi0rSYItFUmMwYSSjdk/E43aezzpBNy1pf+GKEO7hJccfGJmGntL13RwqFBpYy0+RWEgHRvG33nrDyo8UREHA/85N/vQSlFJ0tm5CtUkxzb6SGNDQM2hidshpEkSYqvf/roaltdVXA82MdCyefzMzwvSqlaVcqaNWtYunQpN998c+39iYkJ7rjjDi64YHGNsMizcBjR5ZAnbkvgFwS/HHRwhcKpzGquFJxaCTlUJzrfiFo9bePkl3Ekb1yeZqgUsr0QUAgUS2Mu6zMBCWnYX1pYTG3uXEnDlinbz2GuMp+9BVv5kHDqPRtCY3834tKWJ0pTrSuuV0VIwPPgvC/9EYllB58bsPo568n0tzM1ONFkNLTyLACVlt7N1Rrnvf2S1rr2ERERAAztGgMgTRtdpo/9Ym/T18kYQwdL5lKprzSuU+zYeXQmOh4uXvrSl/Lxj3+clStXcuqpp/Lb3/6Wv//7v+fNb34zYG/srrnmGj72sY+xbt26Wulkf38/V1555aKOJTIWDiNj4934+T2ExlYopJz6XfuypKnmPTYgKISCpGNmTH7GQLenUHiUtcQ38NikAyZk86RiIc6y1Kyfvt3X9tz8DVP2FQ2r0s3LGGDcV3TFrHaEwFrSuQDa2+CkNz6HVa88j1hXZtZtHijKVbz4y7/LD976Dab2joMQ+EagZCvz3eAIcIWpda8E6Dtn1aKMKSLiWKetO1X7Peqhn6RJM8IgBZNDIMjQgYvXskrCoPEpMbB7mKXLug/PwA+Sw91I6nOf+xzXXnst73jHO9i3bx/9/f384R/+IR/84Adry7z3ve8ll8vxtre9jbGxMS6++GJuuOGGRdVYgMhYOKyMD1hxJV8LhGjOKeiamdsCgG8k+UCTmCaXHBiY9BW+kWigEEIhFAwWHJQwSOY2FwSGnphmRTJk3HeaLl5HGOJOQGjmGFADs3eSFORDaDe2jwRQKU00vPDbL8JZduG82z1Q2lZ28trvvZMnf/II2255HD9fYvLuzRWvzPQvpj2PjjBWkCmwhlfPaf20rz46frAiIp5uLrvqbP7r87+sPU+RJUU9/m6MXlA5pSbkg9d8+ajpQnm4G0llMhk+85nP8JnPfGbOZYQQfOQjH+EjH/nIwQ1sHqKchaeIKeTRQ3sxuckFr6O11R7wpL2rrbIkDnqOjyImNe1uiCc0DvbhCo3C4GtwhH2edjST5WrIQpB05v5oBYIzO0PavID+RBFPBrZ80Bi6Yj5JZZALECX15tyFoBRO1z0Akzp53m0+VZy4y0kvO4MX/MNVvPhLv4snG3tT1kVWBRBXBimt/LUxttPnhX/54kM2toiIY411ZyzjOa86Y9awnTEGjaZk8vOINElyjPPzn915KIcasUhEnoUDRA8NEN7wX+gH7wJTUUU8+XScF74auXxNy3UTJ/Qx9eCTyFBzYiZkR95WFyxJWMGjsm5OznOFJuvY2qTp1UVxZeiOBUz4CiUFLs13+o4UpB1JMdQEDd9XT8LKtGRPMcFEENLtBZS1TfAzCIaKHhk34MSMDWe0cpktiS/cnSY9hZM5PA2ZjLGGVEwajDCE2o5TCY2snF9jKsmZBi58zxX0nLrssIwtIuJY4c+/8FoyHUl+8LXfoENjG0xVVB2f0PeQMu2scWbvq2LQlCmRYxw3jLNj+15WruqbddkjicMdhjiSiDwLB4Ae3I1/3YfQD9UNBQCz6UH8z30Y/8f/iynk51y/+yUXQCUJ7/l9JYzRtLl1b91UYD+OqpJjappEcyNCWJllV9bbW09fzJGCtKvIupKMI8m6knZPEa/EB/KBZG/BY6KsiEmIKeuVmApc1qRlkxjUdJbEIenMraHgNeQMCCVZfsWZB135sFDCos/egrSaCgI8ZbteKlk/l0LApklJwfE4/fXnHZZxRUQcS7iew5/+7Sv47uPX8q6/fRmjyZ1sDu9hU3gnE/5e9oVPMsweoF4hYSr/+fjs5HEQEFDmztsfejoPZcEc7mqII4nIWDgAgv/9OpSKtrtTBWMgKBj8KY1/w/+Qf+87KX/vuxg9M6CfWLOU/re8CIBMTPDKlQWUrDvqAiMY82WlqsBOuK2S842xHgawk1+bayqNoZqRFRlmKSrLG0NaGTo82wiqJ1G3datNltKu4Nk9hvQ035MA+hNw4pzaRYaENHXtKSlQcZeTrr5k7gNZZISSDBQkxXD2L6btiyEYLClOu3w9Yla9iIiIiIXQ3p3myj+4mNe841ImGSIX7AMgNCUG2cGTPMgYQ+SZJMc4A2zjSR6kTKkSsgjY+viep/koFkZkLETMixkexDzxaM2jYAyUJwX5AUlpRFEeUxSHFeVxTemH36f49x/HFGf2bV/6O5ex5sNX45ywjFuHNENlv8kj4BvBiC9ndHeci0Zjoi+pa/b7LEeAFHBqpkSbW883iCmbDDmbUdIRh8t6DRd0G07KwrKEYFUaCoFNpjTGNPSzsHkBroCOWF3WLb2mh4u//FZSyw+fhLLyHNrTDr/Z7zJanlatYWBPQXDPqENKQaK37bCNKyLiWObNf/AqPK9Zvj0I8xTJs5etbBePslNsYkwMoWvSjzYJefeuxZUmjlh8opyFBRLc+J9Nz8sTgiA309YyIfg5idi0ifwnP0DyvR9GpNKEJZ/dN95N6cltiGSK/xjPcP+EQhmfU9tiMzox5LWVI1bz2AyNekQpR3BSJmTTpKpqqDUtuzKpSXmQb+hX0phoORtKQV8C4o7VURgtCcZLhgdHDctTgp64HaNBEMvGuOw9z7VjFoLsiUvpOG3506Jd8Nx3XszXP34rd424pJShzbNqmCNlSamSw9ARA+m0VrqMiIhYGEv7ejj7GSfz858P114rh1O4KgmIWk6Dqd1h2H/jZHh865bDP+CnwPGcsxAZCwtA73oS/ejd9ecBBLm5PnArHxyUBO7QPkr/+XVG+s7G//43Wdo5VbuD/4MlCveENL/aEiejSuT19JpYyUhZ0e2Fs9715314ZAK2TGoMmu6Y5IS0YGVS0+FpHhp3KYdWnCEmbX5ByUgem4zR7hpUZaPVsMP8SJSAzpihOy7ZNK4Z8yFouPB/7zOvYO0LDl3Fw4FwzluexS2fv40dYyG5UJAr1I0CAfTGrXdleaTYGBGxaCQSXtNzXxcItY8UzjRDwSYiWy1HhTHzNZ47MjCLEEYwURji2CX84TesHGFlXgwK882ugsCX/N+9y/jx17eT/tmX6O2YapqU+9tD/vKF47zzvDynZX1iImz6IgHsLyl8PfPi2jUF390p2Z73SHtxMl6cXOjy0LjDTwZj3DXiIgV0xQXdcch4YHMLrbeiGuIwpqoSOfeRGAONTS6rSY/rsoKOuAQBTszhNX/9Uk47QgwFsGJNl7zjWazOQE/MClAlFXR5sCoFmbigY00XK5/ZuoIlIiJi4Tz/+TNzk3xTwFAN35rao/reJINccOFZh3WcEQdOZCwshIHtSCkRbmWSXYARLAXkSg4nr57Cc/WM0sdqTt0V50zyaD7GeGDd4+UQMIaMo0m7MFR2mQpkzZrN+/DLYYeuuIfXkJinBChpFdgDA+O+YH9pNitWUDa2n4MBiqGYt49CUc9ejnHqmUt55UdfxIfufDfnv/bI+7Kf9wcXse45J9HmQX9K0J+0oQfXESTak1z5j6+NkhsjIhaRt77tTTiOrBkDSnhIIQlMAV8X0PhofEJTwjc5DCGh8XnT77/iaR75wtCL9DgaicIQC6Gqp+AKqIj4zIc2YIThhGVTMwyFRqSEZX15hp5st+sBRQ0JXdFWEILxwGE8MCjgwTFNxrMfW2MuQPVvKah0U7S6C1MBZJpzjgBBSRtiFW9DLhCkpklKV40M39imUzO3AKm44sI3njv/yXiaUK7iZdddxeafPsb9/3E3o9v2E8vGOeXFp7HxqnNIdiSf7iFGRBxTOI7D8553ATfccCugkNKt5yoQEs56p2Vob18c+fdDjTECM0+TvoVs42gkMhYWguthyiWEEAhP4HYa/KlWH7jBiRlO6m9tKIA1KtpSwbRXBaM+pB1DUlW9EALfWC0Gq60w+/6NqWZN2HHkA+iLG6ZCQaMSQ7VngxSQCyWB0SSVrWaAutfBehVm35fyjvzkQKkkJ79wAye/cMPTPZSIiOOCNWtWI8TPCcMAVyYWtM7AwD46OqPKpCOZKAyxEFatb5qcVUzgttUlhJup9B5IGs5aPTIjD2E6AiiUZ5t0BVOBZF9JMOWDImS4BErIltUF1eZN1W1UtRvaHENCWQ+JxEo+N7rDSloy6lttglDbxk+tDAWEYNWzT2p5bBEREccfJ5yw2vaGwBDoYsvfK2MM2oT8+fsObV+DxeJ4DkNExsICkFe+dcZrXrfBScw0GISCWLtGOaCkYWzUZRZ9pvryAh7bOafCEWBzEBIKumJ6gZULzRhgxJfkA0lMamINSoYpGVLvnWA9CULUxZ5mNYgEeOkYJ1955OUpREREPL28/g2vQinrtA7CAtrMTN6uIoTAD/L89Ce/YOvWHYdzmE+JSJQpoiUqnUVc/JLmF7XBTRninRovE+KmNbH2kHiHRlaDOwLGxm3CwGzfFWPgwa0ZJvKtokGC0NhJvtPVJBUtvRXGNE/vrqhWMFhPQllLTkwXOTmT54RUkWKo2TGpeWwMnpw07C+CRJN1NF1eNTxiGh4Qy8R58Zd+h3jbwlyMERERxw/d3V387d9aT4EQgkJpP2BmlE0CBLpMoK2S4z333P90DDdigUTGwgJxLnsN8orfgXglKa6SpCIkqBg4cVM3EioIYGlHgZ/e1UuxbE+1rpRCag0PPZnl1w/0VJaePaQhMKRUiG/AkXBKNqg0Qpq5fPWlxndSTcmNNixR0gKt4aa9Lr8edthbFIz7MFSER8YFj4zZRM6kMvTHfbJOSFwakimX8999GW+44RqWnBY1XoqIiJidt7/jzXzr21/ixBNPQBufXHEQXU1uNDYMihE4Mk7caQMEX/nyt57WMS8Es0iPo5EowfEAUOc/H3X+8wm3bUL/88cWtI7nGTqTJb78w9Ws78/RnvYpB5Lte5OEvkNGGTxhGCnLaiUyjXkCvhYMlhwGSw4SQ3dMszEb8NCEY1uy1K48m9bY2Jg541JrGlVfyjAZSG4bchitSUo3L/NETpJ24ZQ2jRLQ5lYibTpgxcY+vHRs4SctIiLiuOSqq67kNa95Of/4uS/xl+/7LEp41kiYFkpVMkbMyfDLX97B1FSOdDr19Ax4AdgwwsFVM0RhiOMIuWodwlHzn71KfuBZa0cpo9k+kOb+LR1s3p7FDRVxaXAlpB1YHtd0u5q4BCVsv4ZSKJAYEsrgCGsI7CtJ9pUdntEZcFZHibVpn7UZn/XZEn3xkJi0AkRWiGjmRW2AkbKa0TNh+sAfm5AzLmqhJNv/+44DO1kRERHHLUIIvvOd7+Cq1JyJjkIIHJnAGPjc5/75MI/wwIg8CxEHhBACsW4j4r7fYgJmT28VgKzYCwJOXzHJY9vb2J4v8kS+RC7UxKVgfcZjYzZGQklSwpByDMMlQdHAqqQm3lAokQtgqKQoacGYr+hPaDa0FXCF7U4Zarh5sI2yaVHFgGD/zP5WMyhpwVgZOhucCCbUTD25b6GnKSIi4jgnn89z952bSMX7Wi4nhEBJ76hIcjxeiTwLTxH14quRSccKNCmq9Yj2X2WrIprknTtz3Dg0zr0TBcYDTWBgKjTcM1biP3dPMuHXe0A4wrA8oSuiSXWSClYmrfdguCSRQjBUiDPmO7WGUkvjAXMZCsYYyqENQyQdMW+Tqtm8bU4qCkFEREQsjFKphJQzVOFmRSDY/PjOQzyigyOqhog4YGR7F+rq9yBittRQyIqBIJm1vPHTt3nkwpnqZQYohIafDuUBu2620otl+naqGgpLYiEGQVnbCT00El9LNk2mKGtFm9PY/rUh89jAeLkyfiDpSGaJVADgSs3aJblpA4C+55/e+sREREREVGhvbyced+bVmwHQJuSB3246DKN66kQ6CxFPjYFtiHjlFE7/LjTIFPzoMY8dE9Im98yCAQZLIcMlW6qoWnSCFAKSjm0tnVYh3bEy7V7AUNkjHyqEECQdQbcXklYGrQ2lEMZKhtFSfVjVDnDTEyABpDBcsHqS5d11Y0EoSawrw/KXnL2wcxMREXHcI4TgVVe9CG2COQ0GK8wUIIQgDDWTk7lZl4t4eomMhYNA332Lnf6rXrbp3wUrYsZ/Phyb01BoZE8xJNB2gp+PDi+kM+ajhK2I2F/yaAw/uFKQUDBcMkz4UJ7FnK0mHNW9C7ZUc1lbmVds3I/j6Fq1RaKvnfO/8Ae46emttCMiIiLm5rrrPgWyhEHPMBjsc4MfWs+qJmB8fOJpGOXCMGZxHkcjUYLjwTC+3/7bmOI67UIoB7BjTC3AVLCECEJAajuJNzZ2CgwElUSCjBPia0lobNVCt+eTCyXFUBAagTYCv5VccwUpIOHYHhJdKZ9LTpjgkhMmcKRhIBcj+8yTOfm159NzwTrEfI0uIiIiIqYRj8e55v+9mb/51FdwZAyFhxASYzShsaJM9ZCppqMj+/QOuAUG0VSe/lS3cTQSGQsHQywBxRxUhJZmCx1Ub+gNZl7vQn/CpTq5awyhsQZDaCAXVC9Sgyc1fYkAXytCA+O+YtxXlE3jZG4QolmzYS5ecuoIzz5xbMbrdxVXc/Vnf7eltntERETEfLzpTa/l03/9JQJdJGDuciyjA5LJqBvskUh0q3gQyNMvRGsIW4TY4g6s7ggRVkJp1mUMhi5PknEaG0pZD0OoYSpobvq0PBGghDVERsuKobJDaUYbaYGSkFCm0tRlbk7vn6r9XY2A3PZkG+e9+8rIUIiIiDho1qxZCbOEIWYggyP6N+d4DkNExsJBIM55DuW9mkJRoFtcBK/ZUGoyE6pGQ/VfATy/Z2Y/d20E+8uCMV8wXukImZCamLK6CjvzDtvyLsaI2hcsNFAIYNI3PDlh2J0LGSpqhgoBk+WQ0DSP5KSuIim3bkyM5B3+74Ee9ox0suG5UVfJiIiIxeEP3vpaYHapegCtAz7z2Y8fziEdMMdzNUQUhjgIwsc3Y7Rhz1iSTVszvPiMQbSGami/+p04rUOxOhljW74amxOVsAQIobm8J0PKkYDBFQYpYMIX7C2qJiOjEErygWBJPGAqEDw+6bA8SU2QaW8BRkoQGsNkWTddlNUSzWIY0hGTOFLQ5UEsjHPTPSvoSZZ4ZELxxGiMDe1wTt9+gj0DOP1LD/2JjIiIOOb57HWf5J677+e3v32cxvBotRripS97Hn/w1jc+fQOMaElkLBwE/m9uQy1N4KxawiPfcCmVHM5fO8qyrjxCQK7ocP+ONu7b1s6ZWcGyuMemXJExP0AJwYkpj1MzcTKOIqlCujyfmKom+kBPTLFpKkYhrDuAygbuG42hK2aEquQ0bJmEYkVeIe/rOa1XAxQCzXk9Ck/aV8bLDmmlOSEO6Q5NhxfQlSyh949AZCxEREQsEr/69Y+44467ecWVv8fYqA1/Lu3r4gc//DYbNqx/mkc3P4shqnS0ijJFxsJTxBiNzEzibeyjbwQQhieGE4xPJPCURkoIQkEhEITGlimelHLJOHGqVnW3Z9UY005Ab8xv2r4Q0B0L6fDyPDQep83VZFwbuBgrC3YXFLZ9tWGoWDcUtDH481yMhRCKgcHzbLVECPhG4GLoiYdk3TKeEyKzM0MjEREREQfD+eefy569Dz3dw3hKLEZvh6PUVohyFg4UYwxm72/h15/BW22lFtu7BCedZtUbNQZtBEFoJ+LOWIDANocSwiAbLpUxX2Iw9FQMhel5PVLYaohT20p0eLbplCehJ2Y4sz2g2wuZ8mnq9RAuMCAWmDLtsTKNl25VIbIjU8RZ0Y9aufzAT1BERETEMUok93wA3Hrrrbz0pS+lv78fIQTXX3990/vGGD74wQ/S19dHIpHgsssuY/PmzYs13qefnbfDlhthchwBjOwT/Og/BTueFGhtiyNltQurMQRakFCGuIKykSyJGVYkfFYmfHpjIZ4I51VstHkMzZM6wEmZkGJgCBovvgUmEucCxXm9+7mwb5i04+M0bD8V90n9zlVHdFZyRERERMTh44CNhVwuxxlnnMHnP//5Wd//9Kc/zXXXXccXv/hF7rjjDlKpFJdffjnF4gJaHR7hmNIkbP8luAowbNss+NJfS+69TVDIWU9C0FAVEQB5LSuTriGtNNlKG+q4gqQydMb0vKU0QtBkLFRfA9jY7iMavAPOzHbxs6Kkw3jZIeUGnLd0P56yLgkDdL/7LcTOinpARERERDRyPJdOHnDOwhVXXMEVV1wx63vGGD7zmc/wgQ98gJe//OUAfOMb36C3t5frr7+e173udQc32qebPXdBwgMhKJfgO1+RhAGYhvaMuUCScjShoaEwEhLS4FYWEw3/6tlaO86CmWU5IcCRgjWpgK05x8o+CUHWFYy3SFzojAlcKSiH0npBpKEjUWTfVBJ14jKSF5+/oDFFREREHE8sRunj0Vo6uag5C1u3bmVgYIDLLrus9lpbWxvnn38+t99++6zrlEolJiYmmh5HLKMPVwL7gofvNhQLdhJPOyHLEyX6E2WEMEwFtkKhjiEuzayhhmLY2lgwBgLNrBKjphL/Oq3dR9q4BwApR9Dpzb7djAvLU/ZjT7m2cZUQ0BYrUdSCdW+/fL6zEBERERFxnLGo1RADAwMA9Pb2Nr3e29tbe286n/zkJ/mrv/qrxRzGIcGMbgF0zS2we4ci5Yacni3QFQtqy2kDu/IuuwuJpkZNsxkKMRWyMpUn0JJSaKsbmvZZMTjyoSLUlVyIadvRBto8w2VLi9y6L0YhFBQ1LEtLugLDaNlQDsGV0BGTpBy7nd5ksxiTlIadOcUlKUVERERExEyO59LJp70a4v3vfz/j4+O1x86dO5/uIc3O0H1NwSbHVVzQOUWHFzQtJgWsSPrEVGtpU1dq1mamiClNyg2IqZB6YU5V4REmAoeidgiRNVGmxrhXNddgSVzzyhUFnr2kyLKkQaFJuYLlKckJWcWKtCLtCqSwlRnn9tQ9ONZ7IenwQnLb9i3CyYqIiIg49jCL9DgaWVTPwtKlVsBncHCQvr6+2uuDg4OceeaZs64Ti8WIxWKLOYxDQ2G06ekyUaAkTcX934wQ0J8osy1n2zmHZmajqZ54qdIWypL2ApImoBQqjLET+q58nFKoauspYTuWTfqCtKPJh5LuBn0GKWB5MqRc8UIUtWa8LBt0Fwx9yRJn90zQHms2cvbl4kihMFEFRERERETENBbVWFizZg1Lly7l5ptvrhkHExMT3HHHHbz97W9fzF09DTT0ii4UMXtLsxoKunKXnnVC1qUKTAQOI2VFSVMLSwg0cRUyUfZq7UqV0LgqxJU2O0FgaPcCBgpuLThRKapASqvRoBFMBQ5Jx68NrRjKmnGRUBCPawKjWZudYkmq1BR6qK5TDiVbxlN4EtpPX724py0iIiLiGOF4DkMcsLEwNTXFli1bas+3bt3KfffdR2dnJytXruSaa67hYx/7GOvWrWPNmjVce+219Pf3c+WVVy7muA8/iR7MxCQil4dQE+RnfuKlUBBoG/MXAhKOIa58emI+A8UY+dDGffoSZcrTchRCIwgDFy1DXGWdVZ4MbbihwSgRAlygYJ+RCxVQF3UaLTtNXgyr0yDZNZUm5RjiqoSSduzawHjR5We7syyJC6QjSa/sWtzzFhEREXGMYBC1G7yD2cbRyAEbC3fffTfPec5zas/f/e53A3D11Vfzr//6r7z3ve8ll8vxtre9jbGxMS6++GJuuOEG4vH44o366aDzZMTw4xBqjFJWa6FkNZaNgZ35GF2efS6mTe4Y6IuX2F/2iMuQuJxmAdglAShriZIhUli1Rldq9LTUktDAjinYldcUQ0g5HhvaQvoSVIwHa5Q0Gg0ayaNjbXjjIZ0xn6nA8Oi4y46cixKwNB7S98y1SCdKcIyIiIiIaOaAjYVLL720ZeKeEIKPfOQjfOQjHzmogR1pmJyAYhmA8VGH4XFFVyxECHgyF7eTM8yZw2AMpFWApzRCzGYs1Am0rCUudsTK7C/VDa1yCD8fhImGVhITvuA3ww6eFJzfDQnH9oxQotlgMAZKRvHwmOLBMYFAoKTtJwFwxruefzCnKCIiIuKYxnDwYYSjNArx9FdDHDXsuAOhbT7B5huLjJRcylpQDAQ78zHa3XBWQ6GKFVDSlWVau6GqF6MQkHGbExF/OwKT/vQ1bJZDWcMDo8ZWS1TVJGlWDRsrw4Nj1pVW1WZIOZDobaNtXdRhMiIiImIuDndviNWrVyOEmPF45zvfCUCxWOSd73wnXV1dpNNpXvWqVzE4OHhIjj0yFhaAGXgMMfwIGFsOOb6tTFFLNk8m2ZGPU41kzVcYY02E+a+URlOi8QMqBLAr33oL4z6MlSsGgxEEWuBrga8hFwgeGbeGQr0fhaA/aWg7ua/FViMiIiIiDnfp5F133cXevXtrj5/+9KcAvOY1rwHgz/7sz/i///s/vvvd7/KLX/yCPXv28MpXvvLgD3QWohbVC2HLr+zMWg4Ym/LYPWgrEbSRxIXm3I4p0q6uyDeDH0oCY+/2qxgDgZHEZRlfzxRgqiNQUtfWKev6ciPlhV1oI2XwlKiIQRlrNBjBYMHmO1RfB+iJQU9C0HFS5FWIiIiIOJLo6elpev7Xf/3XrF27lmc/+9mMj4/z1a9+lX/7t3/juc99LgBf+9rXOOWUU/jNb37DM5/5zEUdS+RZWAgjO8AYciMB3/gbTaEsAM2yRJHeWEAxdBkuxtlf9CgECk9pPKmpT+0GV2pSToAjTaU8cjaFcNvCWjU0jRotu5RC61VYaAcSp6LH4BtBWUsCA9poStrUVCATCk7MGE7KWlt33SvOObhzFBEREXGM83S2qC6Xy3zrW9/izW9+M0II7rnnHnzfb2qvsH79elauXDlne4WDIfIsLIRKhuDtP3NIlMBR0O4GCCQlDVUvgTaCfOBQDhXtsTKhMWgDcRXWN4XAlRpHCMraEBpZe0cCcSesJUTmAsVYyUUbSDm2JkLOYWY0cko2IOv6lI1tmR2TmrGyQ4dXL/up95IQrLjsVNL97Yt0siIiIiKOTUzlv4PdBjCjD9J8AoXXX389Y2Nj/N7v/R5g2yt4nkd7e3vTcq3aKxwMkbGwEDpWkH9iM9vvdVCVqEDaqV4wjeEE+3dgIOc7xJ2g5rppFkYUCGGIKYPG5hdIaj2q8LVgoOAxWnLxlEBh8LWgoCXLUoadubmH2hkrcevEFu4c30tea5Z5HVzcfiKr4l3IiueiOhhtBKGB1S847eDPUURERETEglmxYkXT8w996EN8+MMfnnP5r371q1xxxRX09/cf4pHNTmQsLIRV5/LIv2+lOs8mlaZ1RYOgGCoSTr2r42zLgMEVmpgXUg4Fe/NxdufjlLU1MVwBHgYh7KQek3BiRlAIDMOlmVsM5Sj/PX4rJe3XbN+9pUnunNzOCztP4Yqu0wBJRgV4UrOv5OEkPVZcdOJTPjURERERxwuLqeC4c+dOstls7fVWXoXt27dz00038T//8z+115YuXUq5XGZsbKzJuzA4OFhrvbCYRDkLC2H56TzxQL1Hgytn5rRW20VX0woWptIlKIWSu4fauX1fJ1unUpQbkh+VqOc+CCFwJLhScHqH5MxOyZI4ZF3ojsEpbZrb8r+ipIOmkenKsxtGHuWBqV0ATAYOpYpBcvYfPRsn4T218xIRERFxHLGY1RDZbLbp0cpY+NrXvsaSJUt48YtfXHvtnHPOwXVdbr755tprjz/+ODt27OCCCy5YpCOuE3kWFoKBUrFuV1V1C4SAQigY9x1K2lY/OEKTdULSjp5TpGk6U8HsH0NcGWJSE5OQDyWBlrjCEApJVwy6YnW1xTsntpPTs7gbKggEt4w+zpnp5WggFzqc+67nsvFNFy7wJEREREREHG601nzta1/j6quvxnHqc0VbWxtvectbePe7301nZyfZbJZ3vetdXHDBBYteCQGRsbAg9IN3ku4wjA3amb+oJVmhmfAlo75LNVEQIDCCEd+hqDVpRxJz9JwGgzb2Ln8mBolNaqwmOyaVZsrMrUu+pbCvkvw4u4/MYHiyOAKEgKLtxG7O/INLDuQ0RERERBzXPB2NpG666SZ27NjBm9/85hnv/cM//ANSSl71qldRKpW4/PLL+ad/+qeDG+AcRMbCAghvv5l15xju/JGtLihrQc4XjPrV09ec5DjpGwYKgv0lj3M6i8Sk7RQ5HSlgsBCj0dioBjC6Y0HNyKgaDK7U+MHsvRtcOV+NRHV0tnwys+Qo79URERERcZhpVMM9mG0cCC94wQvmbLEQj8f5/Oc/z+c///mDG9QCiIyFBWAGdjI61UnO90g5tv/FxCwegSnf8Oi4YbKm0Oxy136HMztCntHpE3dCHGnLKaWAUijp9HwEkAslAkgoQ9oNa1UXVYSwuRJB7ZqpGhjWuDgl1ckdEzvnPAYBrIi14QqFb2D1c084uJMSEREREXHcECU4zkOw5TEo5Ljjtx47cmAISTshmmaFxnxguGek0VCorG8Ed48ofjXkUggdioFk0nfYX3QItCTtalakSvQnfJYmAtq8mYZCFQGc2j+CVyvbtGWXhVDzrPYVJKQzZ1qlAV7YtQ4NxNokq65Y/JhWRERExLGMXqTH0UhkLLTAGEP47c9ATOFNGZ7d67M0rvFkYy8Iy9YpU+veOBPBg+OKibKgpB225+LEVXNJpVVtnNs/ZYztLplOlXn5u0/FPftEHhyFu0dgT16QUC7/36oLcIVCNpgM1b+f37GGZ2ZXIITg+Z++CDeZfOonJiIiIuI45OlUcHy6icIQLdCP3ocI8+ybbOPEWFCrgBDGhgT80NpavtbsK5qW5ZIC2DypOLszJFPpI9FITGqCcO6PQwgYKSvu2NqF+cdNTE4GFCt9I8Z9CDSckurmb9Y9j5+ObOWO8d34JmR1vJ3Lu9ZyenoJuUBy4qWddJz33IM+NxERERHHHYuQs3C09qiOjIUW6Lt/DsCTjySAuidACoMnYV/RsCsP+0vz6yrYvAS7jCs0oaEp3OBIQ8yElLSiMeGxaqAMFBSFUFHIKVJugCthVQq256xi5Pac5IS0psdL8Yalp/GGpXVVRtvECuKe4Zy/fgdCRA6liIiIiIiFExkLrdi3i1JRMTHmNr0sBewvGR4ca+1NaMQAKWVNSo2gEChSlT4QVeJKo4ShpCVhJSqRCyTDZYdcUJ/gh4tQ1rZh1EkZzaQPj09Isq6hJ25qBkbVAg4M7Cm4dKYlYrayjIiIiIiIeVmMnIOjNWchMhZaYTRhOHNyDQ3cOyJr3iQhBBLT8iIwwLqsNQ5ygWLSSBJOiDTNuQuOMLhOSDGQjAYO2/N1dUVjbDXE7ny1DsKwF8GKhOZ5SzVSGAJdEYIydp/awN6iS4jAlAP8ySKxjtTBn5uIiIiI44yno3TySCG6zWxFtotEl0A5zZ/urrxt/9xYDeG0PJOG09tDMg7kAsGEL5kKHHbmYhRDWbl4DFJoHKkJNZS0ZHvOIR/Y9tSBthfZeLm6RSr7F+wsSB6bkFgVBUFoBIGx/2ojiCtbu+E4EicZSTtHRERERBwYkbHQAnHORbjLUyzfqJvO1HhZVKoh6kgh8OTM9lKuMDyjM+SC7pBcIHgyF8c3At/AiO/y2GSCCV+SUCEpJyTphLTHfJbEi3THfDSCEChqQV4Lxv1ZR8r2nKQUzvaeHbojDMuftwEVc2dfKCIiIiKiJcdz6WQUhmiBShiMgJNe28Pw4BS5Pfa2fi4vghSCmAJtbN7AxUt81qQ0roTdeZfhskJMK4NIOwH9ydIMI8NThrM68yhh2DxlEyyNgawrGPdn+rEMsK8kWJFsfk8IGzZxFWx4cyTvHBEREfFUMcbMqaZ4INs4Gok8C60Y3YURAmf9ci746ApWPDcDQH9Ct0xslAK644Z1ac1koHh0Is5w2Z21CuGEdNEGE+bY3KltBRxhbVEhBHFV/9AkBqdBnyHQzRupxteKWrDxdc+gfd3ity2NiIiIiDj2iTwLrZAK0dWJSSTw3Ak2Xt1J37NSfO99g8RkSDGUMzwFFkHaEdw9msAgcOXsVllMatq9YJZ3GoYgYFnSZ3vOti8VQnBOZxltJFnP7rsU2jyKtDPTqwDQkZQ84z0vPNCjj4iIiIho4OloJHWkEHkWWtG3HrrbEENDYKwvYWxK8OshxdZcmbKuRp+aP/3+pCDtypr3YS7PgbeA5k8GSKjm5TKO4YR0uZY34Uk4IW3IuGJGpq0xsGxDL1LN3oAqIiIiImJhmEV6HI1EnoUWGFGCbBLCsBZ0kAqmggADDJfLJJVkXSaGBuJS0BUHVwoCDb4GMGhjEyKneyHKen5bTQCFpvJNQ9bVBEbgipCSdmqGiIHK6/XLUQhmb3kZERERERGxQCJjYQ6MMZB/FCo35AZACFascwkbJuPehGRFSswwBBxpQwiBFhhjEHKma6GkJWNlhzY3mDNnQRubHFkdRcbRPDgRqxkanjR0uIa0YxDCGguOMfXtCUgt63jqJyIiIiIiAojCEBGzML71SXCxt+ZSgrKdn9yEQ/+qevlhUsk53UpSVConpLCKjEwX5DA8ORW3rqk5NvLwRILACECTdgyeFCSVoM01JJTB1zBYkoyUq7oP06wOA6tffu6Bn4CIiIiIiCaO50ZSkbEwjdGdo3zzd7/OT/7qv+0Ljbf8lb/f8u6u2ku+mbsuQld6MoCtrQ2mXSRK2JBCznfQpnkrgYaHx+JsnYqTVIYlMUNS2X4SUth/4xLaXLudUb+qs1DfiTGQXr+crrNXH/B5iIiIiIhoxuYcHOx/RydRGKKBicEJ/vW1/0J+LM/y9Q3iRdNiBKefk+TkDTEef6TESEkjsrObC2FFmbHWFAoIqn9gQwbtbkiIYjKQaAPjvsI3AmGgzYNzOkuMlBXjZTkjVCEqss5pB8Z9w7gvWKpsCEIbyOHyon/8vTkqNiIiIiIiIhZG5Flo4Df/fDv5sQImNLjxFu2mheCv/2kFZ5xjQwRT/swwgjGNksyzY5Mkq/aaYKDosb/sMuG7ONLmTWgDk/5cJZrWYFDCNpUqa8FkIBgsu+wpeVz0qdcSb4sfyCmIiIiIiJiD4zkMEXkWKhhjuO+/f4sJbZliMtvajkqmJJ/+4gp+9v08P/zcKAZqSYYwf8vqKrnA9oYIDeRD299BYHAlGCPwtZh3W8ZYY0EbyGvFmkvW8Yy3XUzfmSsWNIaIiIiIiPk5nhtJRcZChaAYUM6Va88nhudotDCNC1+c4af/PMZwwVAI4PR2289BGM3OYqy2nCMMXV5AQmkMMOErxnyFElDSYpoXwnoUQkMtMXIhLE+VOePcHjb+0+sXvlJERERERMQ8RGGICk7cwWvoyLjjoTLjQwGmlc9ICOJJxfOvtqWJGuiKhfTEgqawQbsbcFq2QH/cp8MN6XRD1qTKbMgUSakQX8uGD8KQVJqwkvDoCCvr3ErKQwgIgZzvMv74nqd0/BERERERrTEY9EE+jtYUx8hYqFLO8+Lf6+KS5xQ5+5wS8Zjmhs+PAS2maccBIbj0DW1c/rZufKAUWj/VUElhJ/6QNclyTcWx+gCrkdDuhQgR4kmNKzSO0MRrYosCISDtzK30aIytnGhzAAFDxagFdURERMShoBqGONjH0chxH4YwxsCjP4MHbuCUnhDdaWUVnv+iIgNuFm1sAmGTxSCENRSUrDwVPP+NGc5bO8K91xkcNEWtEEBvLKitMh0happPLE3lGSlLpJipl5B2rGJjPqwOpCL0VNloTFZFGgWTYSTrHBERERGxuBz3xgKbfgX3/RCw07NSdjpWJ69gWTZdNwyqTHcPVDBP7uXJ70yRVo2NoQxtbjinOiNYK1NhtRNcaTBGoqclNAoB7W5IUmlygSQwhgCJnGUo5cCg9w8ju7qf0umIiIiIiJgdXXkc7DaORo5rY8GEATxww8w32tLQliEoG0Y3F9Easick8SdDdt0yRnF/QKzdYdklbbSts6WJ4z/bib/PQ1Um75QKyYViQTURShoKgUQJgV9xU82mqRBThpgKCTSM+DMjSMbARFlS2DNEKjIWIiIiIhYVY4z1Rh/kNo5GjmtjgcHN4BdmvGy62nnsv8Z44kfjBMWGpkwYXFWpW5Cw/YZRlj4zw+kX5xjaQs1QAFieLPH4ZJKiFsSlaeldSKqQXfkYSakpBIpZ2kjUx2agOEcDKiFgtCx4+J5hzts4/+FHREREREQshOPaWPAnJnFnef233ymx45f5Ga8boBxWujpWbIiBOyZwhwoM74mTFXUHU4/n42RzxKSp6CTMtACMAYQhG/PZWUihHE1eC1LKbn+6gVE1SAPd7H2o/r2/BGUNk7mj03KNiIiIOJKJGkkdh2ht+NFn7pnx+sg+NauhYLGzc9jYx8EIdj6RYDxXTyw0BgIjSTnaehvsq0zv2yCEYUXXBMt6x0nHfSZ9BQgKYb05VWP2rMEKN437gnxYv3ALIewpwJhv9RrSmUjeOSIiImKxOdiyyerjaOS49Sw88asn+O3NQ1xwsqS7R1eqCWDHliRCakzFSdAYXrJ38mKWD1swVnZYkfIBK6RUTVK0FQ/GJsbUjAxD3A1Y3j1JJmGFoE7qGee2rUtxpF03F0ocYZtEAYSaSvdJayiM+XPIPwNLV7cdzKmJiIiIiJgFwyIoOC7KSA4/x61n4cH/e4ikC7ffGEdr0BXjIF9wMLrSMbIyQdtH3f0PMy+YklGMlxWTvmIqcGYYGapS7eBUtBSUNDVDAaAnVSbu+dTDFXa/JS0paUlgZO29XDC9lrNOmwtepv1gT09ERERERESN49ZYmNo2TJejGd7jcOe93QQbTsKcv5FYp4fBegeap2Pr4g/mqFYohIKHJ5Jsy8VxxNwJjbbUURDMoofwnBP2Mj1c0Ywh1NDhCZzp1RIYOlxDe1zQdVLvAs5ARERERMSBcDyHIY5LY8GUxjB7BwA4/dVtPPNdfThtto/D8ovSBLU8xekz/sycBYMhHwgKgSClwBU2jNDKVWUMSDGz2jbmGM5dMzTLfi1SAEITV4a+OPTGDF2eoSdmWJ6AjGvY8PIz8FKxWdePiIiIiHjqRAqOxxgmDGDLfZixIYgnEevOtmqLT9wOW+/A5Md4yWth+/YkZ7yxvWlqDgOrkDg3wuqDG5DSEIaCrVMOiYqjQArwtcRzWjeiSsT9Ga/5oSARDzjthBG2jS5harQu8LTkBMHGSwQD3x/jnqE2yhpiSmDNAnv1dXQqLnrfFQs5RREREREREQvmmDMWzKZ70Tf+KxRzVgzBaMxN34L2NCKtbBgA6FgCHc/sqK0XlA3b7y2w7Vd5iloQaxFKAIFQoNo19z0Zx9eSrkr/hmqOg6/FrOEIYyoNpzJ1fQc/FNw30MmW/RlCIwFDIqZZfvHJrHn2SfSft4b0/h+gx3ZRmGpnxU9LjE5IcoEiMIKEozn5DMNwoQ0nMVsxaERERETEwbIYYYSjNQxxTBkLZtvD6O/9E7WYf7WkQWsYmcD4MURnErC5AyaVAD/g4Zvy3PaNCUo1fQKFwJB1NQk1+wf7zJdMcctdSSSGDdkiCsl4ZfL2tSBnFAkV4jUIMtmSSoFQmnTFsxBowU1P9jGSj2GwBoYrDCKA3bduYvetm+je0MfK87t57PsB40MCISr+BCPoWRLygj902PG45OH/Gmf4kT30nLrsUJzeiIiIiOMabRbBWDhK4xDHlLGgf/FftCxMmSxhlmQQ2ZQNS+wb5pHfGH72zZn5AwYY9xWCkPg0gyHZDmm3TMeU4qLuAG0EJa0YD+zpHPcdOr2AQuhQ0gYlrCxToAUhgrQsk8t5JBI+W/Zn2J+PQcVQ8OTM8Q8/OsDwI3sJDNgmUnV3xfCQ5IefDzFBCEjyQ1MHeNYiIiIiIiJac8wkOJqxIdi3Y/4Fx/K2jlEKdGi47b81oYGpAMbKMO5DMaQyIRsmfNmUkOLJkLXtU2y50aU7NKTckJQbkHF84jIEDKERjJQdiqFAG0FgJGUtyYWS/SWFNpDPe4yOJdk8mq0egVWGnPXgjE2KnPUtwdiIYvXJtgwztSRzAGctIiIiImKhmEX672jkmPEs6P17FrScKYeI/RPQ3c7Ox2B00hoIjR6JQihQwtDpAUIQCFvlEHNCTmzLURqBnB+ryDjX11uZKrIjF6OoHUIDE4Fi3Lc5EqYi6AQQVxoQ6BAmiy5Vr0IrRG1XsyRgCsPIsKJvQ5auU/oWdB4iIiIiIg4Mw8F3jTw6TYVjyFhgYMvClgsriktTBbZtiTUYCs0TcGhgtAzdnqH3jCQrz03hPrKXiUdhym9MImxcz7A8WaYQhkwFCmNgpKxQtc5QBkcYumLl2rqO0JSNqqg4tEY02yYNuxWUipJnv/MURKuOVREREREREU+BYyYMIZTbuuKxSlULqVDisYeqAkizrVipajCw4nTFmmdnyD+eo+Cr2vuzrSMFJFRIT8xnSdwnVst3sP+elJ1q6irZnyjZZMsFDX52hDC0d4R0bVjxlLcREREREdGaSJTpWGDtWeDMczhKgOeAsPkKO7dCawvDUAjhtn+dYt+9ExgNgZbzrmMdAIaSFpV+DpB1AzZ2TNA1TV9hVbqAG3cI57EVTK3b2WzdKwXrzwygd23rjUREREREPGWMMYvyOBo5ZowF2bPCGguunH0ur7wnHesZ0A19HuajFEpu+ofhij24MA+AQHDi8lHOXzHBed0jnNE5QbsXzFgu7hiu/NxryHYn8c1c2zYIYeZobWqQGEzvyQgvsbADioiIiIg4YJ4Oz8Lu3bv5nd/5Hbq6ukgkEmzcuJG777679r4xhg9+8IP09fWRSCS47LLL2Lx582If+rFjLACwbLmVUHSVNRocWTcglIBUrNbUwUk6pDMwX7qJazWSmKg0iZpveWME2tiqiY62MivPSBFz5lhHCjKXnMnSZ67jd7//di47fYAlbXnktGTHdNznkvV7Wds7MW3/BreSfLntyajTZERERMSxxOjoKBdddBGu6/LjH/+YRx55hL/7u7+jo6MuKPjpT3+a6667ji9+8YvccccdpFIpLr/8corF4qKO5dhJcATkea9Cj38BCgH4YT0dwVWQcJBS2r+NgWScNWvgwQfm2pqdlNMONY/C1qkkJ6YL+POEInYV4rhSk92fYPWSPOlnncnUrfdZbYdQW4NGGxIb1rD0z95gx55Os/r1z2fZj/4XvyzYO5rEDyXpuE93uogQ0J0qMpWLMZaPVUZUHakhv3f8YE9fREREREQLDreC46c+9SlWrFjB1772tdpra9asqf1tjOEzn/kMH/jAB3j5y18OwDe+8Q16e3u5/vrred3rXndQY23kmPIsiGUbkKc9B5nykO0JZEfC/pvyrKHgSDtRp+PgKE7ZKIhLmNnp0f7d5VmHhK68Nlp22ZGLVSbp5g+8Goca9R00gqKW3LOthwce8+j/yzez7JN/jHPeGTgnn0D64jNZ9ldvY8Xf/CkyGa9tw33xK1DPuAglDMs7c6zpmaQnU6wpQCppeNbJe3CVRopqB0v7iCUjmeeIiIiIQ8niBCHsjDIxMdH0KJVKM/b3/e9/n3PPPZfXvOY1LFmyhLPOOouvfOUrtfe3bt3KwMAAl112We21trY2zj//fG6//fZFPfZjylgAEOe+Ei77I+jss4aBwM74cRcSHrQlIR4Dx+HEZzgsiUOHS0PLZ0NcwpIYJCtOiMZ6iX2lGI+OJyiEzZ0lAyMYKrmMV8oqRWWNzVtj/OwdX+O7f3I9N/3vTm76xQR3b4ZRnUCo5tMvhECPT2DmKH+UAjxHs7JrsrZvUS3H3BhVQkREREQc2m8XqgAAIBtJREFULaxYsYK2trba45Of/OSMZZ588km+8IUvsG7dOm688Ube/va38yd/8id8/etfB2BgwHZP7u3tbVqvt7e39t5icUyFIaqI/lOg/xQAzPbbYMvP7KyvJEhZux1Pt8PG50gevEWTceveASHqyY8hdUOh+m/JKDZNpnCEJiY1SlRWmjU0Ydhx+3bKum4YDN6/ix+949tc+pGXc/LLz6y9HhbL7LvtCUZHOyn7CiU1bdkiXR15XLcuBdKTLbB1KEs9GCJY9pxTDvKsRURERES0YjHDEDt37iSbzdZej8ViM5fVmnPPPZdPfOITAJx11lk89NBDfPGLX+Tqq68+qHEcKMecZ2EGKy+AvtMg5oHj1I2FChe8McWS5bbYUQj7qGLFm+2EPJsZEBhJLnSYCFRTv4ZmxIx1jbbuils/8gOKY3m7r0KJR//sSwwMpimV7faCULF/NMmWbV0US3W7TgmDwnoaGhIXIiIiIiIOIYtZDZHNZpsesxkLfX19bNiwoem1U045hR07bGuDpUuXAjA4ONi0zODgYO29xeKYNxaEkHDKy2HtCyDRWX9DKUgmkIkYTiGk09OkHUNSGQSGAKuYMJdo4rS9tJQAnS3HAUAHmk3/dz8AO796I1OP7mCmaSLQWrBzd1vN2zGai1eEnOrE2pPzjjIiIiIi4ujhoosu4vHHH296bdOmTaxatQqwyY5Lly7l5ptvrr0/MTHBHXfcwQUXXLCoYzkmwxDTEUJC/9nQfzZm162w/8Gm+dhUChSq3SVjyjBcUgfkbmqlviAFJKShoJuXFFIw+sQQYaHEvu//hjmEFABB2XeYynkkkz7bhzO1kQkp6DlrFckl2TnWjYiIiIhYDHTlv4PdxkL5sz/7My688EI+8YlPcNVVV3HnnXfy5S9/mS9/+cuAzXO75ppr+NjHPsa6detYs2YN1157Lf39/Vx55ZUHNc7pLLpnIQxDrr32WtasWUMikWDt2rV89KMfPXJUq5aeC97/396dR0dRZ3sA/1b1mq07ZO00JiFhSZiwRYWYKKKSJzLIgDIMRo46iIIaUZZxBN/RCB4miBxghsOIvseA76E4MGdccBw4MCNwYiAsBpGwSHghIWQDYvall7rvj07aNOnuJKRXcj+cOiRVv6q+9SNN3/zqtwSj64d2+DAFhC41IROACJUZgxQmh48gfmaZFMnZ2g5ixzWVdi6kCFSi7cp1SG2G7gdvep3WNgWOX4qCwdQx5XRH34txL0/u4VzGGGP9RQKBBKmfW+8/C8ePH4/PPvsMO3fuxKhRo/DOO+9g48aNmDt3rrXM73//eyxatAgLFizA+PHj0dTUhL1790KtVju5ct+5vGXh3Xffxfvvv4+PPvoIKSkpOHHiBObNmwetVotXXnnF1S/XZ4I8EDTs18DVQ6CGEggAhmUGouq07Ye1UiCEq02IVplQ2qJEu+QobRAwOKAV9Sal3dcT8XMXCYVIMJhhvQ6ZJSRk/qLbqAgHkaPkuhbVTWp0PhsJiAhG+sqZiBoX16t7Z4wx5l8effRRPProow6PC4KAVatWYdWqVW6Nw+XJQn5+PmbMmIFp06YBAIYMGYKdO3fi2LFjrn6pWyYogoAhvwQMjaDTWxGdYEbyr8Nw/m+1EETLYwlZl+wvXGnCDYO8I2Ho7PJo+TtCaYBWIUEuGlBr+LmDSmdq0XUUZOdITgIgyAREj42FLjUWMEtQhIXAWNvoNO67Ni3ET+WNMDa1ISQ2DLp7hkLsVaLBGGOsv8gFoyHIT3ujuzxZyMjIwIcffogff/wRI0aMwPfff4+8vDysX7/ebvn29nabySgaGhpcHZJjghxCx2unPBmJiLEaFH95HTfOtkA0SYAEy3oNgoCkkBb8ZJCjziiHRAJUMglhShMCOvo5hMjNuNEuQS46//CmjlxDlxqHKRvnWJaUlsugz3oApZv32D9JJkIzeggGjUvEoHEuvH/GGGO9JkHqoTt7767hj1yeLCxfvhwNDQ1ITk6GTCaD2WzG6tWrbZ6xdJWbm4uVK1e6OozeaesyRXJbK6LHhSAqNRhQq2GoMyLvyRNoN4vQKI0giAhVSghV2utbIIAgIEhuQptZCXtzKhERBAEwCQJi7h6C6VuftiQKHXSzJ6K1/DpqvjjSbVrowCHRGL7qadffP2OMsV7rOgNjf67hj1yeLOzatQsff/wxPvnkE6SkpODUqVNYvHgx9Hq93UkkVqxYgaVLl1q/b2hoQGysh2YjpC6rQDa1AEFBliGVkgSFVoFBqRrUFbT0uheoVmFGgxFQybofEwQBokAQScA9ix6ySRQAQBBFJC6bhcipd+PaV8fQeuUa5JpAREweh0ETR0FUDIiBK4wxxnyQyz+BXnvtNSxfvty6gMXo0aNRWlqK3Nxcu8mCSqWyOxmFRyiCQKIIRIQBAQGWZwQmE2rybuDC9iq0XjMAkMMoWXKIHi8nEoySAKVoOxOkIAAiJMgAjJoxGvpUx8lQyC/iEfKLeNfcH2OMMZeRBAmCwI8hXKKlpcWyaFMXMpkMkuR7FUSqYECnsyww1eHqgVqc3VIBoKM1gAhVbQokBBlATgdREqjjaZaEjvEORB0TO1kWfpKLhLiMRDfeEWOMMXfhPgsuNH36dKxevRpxcXFISUlBYWEh1q9fj2effdbVL9VvQmspSPFzk0Hdjy04u6XCOt5BkoCfjDK0SQJazUaonbQuCCA0mWSQCWRdw1IEoBAlyAWCQRKgBKBJjHDrPTHGGGOu5vJkYdOmTXjzzTfx0ksvoaamBnq9HgsXLsRbb73l6pfqN2r+P+vX5nYJJ965DKBjeCMBNe0yNJotyzVdbFJhrLYVkp3Whc7FnK61K6BVdCwsha7DJgUoREsCoQwJcNftMMYYcyNuWXChkJAQbNy4ERs3bnT1pV3P3Gb9sjKvHqYWqXMAAspaFGiVZOhcgcFIclxsVmNoUHvHbI2WGRMIAgQBqGxVQC4IkIuOx9CSAAREh7r1lhhjjLkHj4YYqAS5dS3q2tMN1g6J5a0KtFqXlP65JaHeqEBRg4holRFahRmCIKDZJKLOIINMEBEot58oEFkSkJiUGIjyXvSUZIwxxnzIwE4WJKPlWYHRCJIsi0f9ZJDDDMG6wuPNH/9tZhnKWmUQWwFBIChACFWarSMg7BEEy6yQqavmuO9eGGOMuZUEMwSY+30NfzRgkwUiCTA3AhBARiOKS4DrBjmInC0J1ZE8dD6FgAClaLZ0hiTLYlGdQyUtr2H52iwBgxIGITCWOzcyxpi/oo4Jn/t7DX80YJMFAB2PIAil37Wj/KKpx0TBeho6EwHLYEq5QJbWiI5JF9GlVcIsWf5OeWWqm26CMcYYc68BmywIgtgxbwLhh73NlgWk+tA6RCDIAEgQcUdgI0pbgi3JAXXJFiBAEAWEDotGzH1JLr8HxhhjnsOTMg1UggyAGbVXjZDM3fsnOGYpKRcI+sA2BAcQ4sUWlLeHwGyUIHRM90hmCYN+ocfE9XN5dUjGGPNzlj4L/fu/nPss+KOgYUDTeahDLElDX4gCEBNkQOLwQASlpCBhWgbuGhaL8gNF+Ol8JWQqOfQTkxAxLq7bOhCMMcb8Uf+HToJbFvxQ6J1A8wUkPRiMygu1fWhaEDBhzmjc88avINw0FDJheioSpqe6PFTGGGPMWwZ027goyoCwBzDyPzQIjuhtVRBkMgETVnRPFBhjjN2+JDK7ZPNHAzpZAAAx6A6oYjPwm/Vx0EQ6+/C3jJwQAKTPT4dMwYkCY4wNJJ0zOPZ380cDPlkAADFoGDSpWZi3+y6MzAzp2Etd/rZ8HSgDho3R4a4XJnkjTMYYY8wrBnafhS5EUQEx6hFMXXcPxh0/hsPrT+PaxRaQGVCJhIBAOZIevxN3LpoMuVrh7XAZY4x5GMEM6ufv2MSjIW4PgjIU+nsfxhP3PgzJZEbdpWuQTGZoEyKgCFR5OzzGGGNeYpkjgedZYDcR5TKEJem8HQZjjDHmVZwsMMYYY73Aa0MwxhhjzCkic8eYuP5dwx9xsuDDiAg/FV1F7dmrEBUyRKcNRZB+kLfDYowxNsBwsuCjGkuvo+CNXai7UAl0rk0lAHdMTsFdbz4GRRB3tmSMMU/iDo7Mp7Reb8TB5/4bhoZWy44uUz5c/fdZtN1owqQtz0LgxakYY8xjLEMn+/kYwk+HTvKnjQ+6+Ek+DPUtIHP3DJQkwvXCUlQdKfZCZIwxNnARSS7Z/BEnCz6o9KtCkOS4x6wgCijb+70HI2KMMTaQ8WMIH2R9/OAASYT2G00eioYxxhjAfRaYjwmI0qClou6mvQSZYOnlKIkiggbzqAjGGPOkgTx0kh9D+KDEx8cDQucPJCFYbkK02oAotRFRagMi5G0IDVOCyD8n92CMMeZfOFnwQUNnp0GTEAFBBoQpjQiWmyF2SWblIlC18yAu//kf3guSMcYGmM4ZHPu3+ecveZws+CBFkAoP/Ndz0I+KgVKknxsZblLx6WE0XSj3bHCMMTZA8WgI5nOU2kCoze0QnP0LyURU7SnwWEyMMcYGJu7g6MPayq/DaYuVWULr5RqPxcMYYwOb2QUPEfyzgyMnCz5MFqiC1G50XEAUIA8O8FxAjDE2gFkeIfR3NAQ/hmAuFpE5DhCd/BNJhIjJYz0WD2OMsYGJkwUfpp99H2RqBWyGQnSSiQiIj0T4pNGeD4wxxgYg7uDIfJI6JgwpGxdAGR4CABDkItCxeFTwcD1SNi6AqOQnSYwx5gmSi/74I/6k8XEhI2Nx964VqM0/h/r8H2C+dh2aoZEImzMFinCtt8NjjLEBg/ssMJ9mLi+D4X+3gQ4dhnj2HJr2HEbZU/+JquXvQmp2vo4EY4wx//T2229DEASbLTk52Xq8ra0N2dnZCA8PR3BwMGbNmoXq6mq3xMLJgo8zV5Sj8ndr0VLeApuMloDm7y/j6qJ3QAYnIyYYY4y5BJHZJVtfpKSkoLKy0rrl5eVZjy1ZsgR79uzB7t27cejQIVRUVODxxx939W0D4McQPq/hv7bD0Gzb7NVqkqG+XQkJAlQlrQj68hDCfp3ppQgZY2xgsEzV3L/HCH2d7lkul0On03XbX19fj61bt+KTTz7BQw89BADYtm0bRo4ciaNHj+Kee+7pV5w345YFH0YtzWg6fQWdMzOZJODHn0JwrTkAcoGgFiSYzSKOrTuA8n3fezdYxhhjvdbQ0GCztbe32y138eJF6PV6JCYmYu7cuSgrKwMAnDx5EkajEZmZP/+imJycjLi4OBw5csTl8XKy4MOosR5mowBAQLNRxMU6LaLURmhVRihEgkwkqGQSogLaUbb6f1BTUOztkBlj7LblyqGTsbGx0Gq11i03N7fb66WlpWH79u3Yu3cv3n//fZSUlGDixIlobGxEVVUVlEolQkNDbc6Jjo5GVVWVy++dH0P4MCEoGDK5hLY2AedrtUjUtEIQCN174xLUMgmXcz9G1Oc53giVMcZue64YydB5jStXrkCj0Vj3q1SqbmWnTp1q/XrMmDFIS0tDfHw8du3ahYAAz87eyy0LPkwI1kAdrURZUwg0SjNEu4kCrPvk9XVoran3aIyMMcb6TqPR2Gz2koWbhYaGYsSIESguLoZOp4PBYEBdXZ1Nmerqart9HPqLkwUfZ4oNx09tSmgUPY14sCQMlZ/luz8oxhgbgAiSS7Zb1dTUhEuXLiEmJgZ33XUXFAoF/vWvf1mPX7hwAWVlZUhPT3fF7drgxxA+ri32TphxAoIA9GYykLqiMrfHxBhjA5ErH0P0xu9+9ztMnz4d8fHxqKioQE5ODmQyGbKysqDVajF//nwsXboUYWFh0Gg0WLRoEdLT010+EgLgZMHnxc+djuN/PIF2swiVzATHCYNlxET1hVqPxcYYY8x9ysvLkZWVhRs3biAyMhL33Xcfjh49isjISADAhg0bIIoiZs2ahfb2dkyZMgV//vOf3RILJws+ThRFKAIk/NSqQIjC7KCDIwAIMElAYyNP0MQYY+7g6ZaFTz/91OlxtVqNzZs3Y/Pmzf0Nq0fcZ8EP/Mfny2EkGWrbFR17uk7q0fE1EWpaVZBkMk+HxxhjA4Tkos3/cMuCHwgOD4FyWDRqfqyGWRIQpjZALv6cMLSZBNQaVGg2yRGeOtiLkTLG2O3L0y0LvoRbFvzEzL++DANEXDcoUNoUhLKmQFxtDsSV5kBUtQagyaiAkUSM+PV4b4fKGGPsNsPJgh+Zm/cGTIIMbWYBTSYZGowyNBllaDHLYCQBkalxiH0wuecLMcYY6zNvD530Jk4W/IgySIUnD72O6HuGgiBYN0EmYvhjd2Ly5qcgyrnPAmOMuQMRuWC6574tJOUruM+Cn1GGBODhLc+g5Vojbpy5CghA5NhYqAcFeTs0xhhjtylOFvxUYGQIAvmRA2OMeZAZvZkczzluWWCMMcZuW5aRDP1LFvz1MQT3WWCMMcaYU9yywBhjjPVK/1sW+DEEY4wxdjtzwWMI8GMIxhhjjN2OuGWBMcYY6wVywSMEV1zDGzhZYIwxxnqF+ywwxhhjzClywWe9fyYL3GeBMcYYY075XMtC54QVDQ0NXo6EMcaYr+v8rPDMZEfkt30O+svnkoXGxkYAQGxsrJcjYYwx5i8aGxuh1Wrdcm2lUgmdToeqqiqXXE+n00GpVLrkWp4ikI/NPSlJEioqKhASEgJBsN+RpKGhAbGxsbhy5Qo0Go2HI7w1/hazv8ULcMye4G/xAv4Xs7/FC3g3ZiJCY2Mj9Ho9RNF9T9bb2tpgMBhcci2lUgm1Wu2Sa3mKz7UsiKKIO+64o1dlNRqN37yZOvlbzP4WL8Axe4K/xQv4X8z+Fi/gvZjd1aLQlVqt9rsPeFfiDo6MMcYYc4qTBcYYY4w55ZfJgkqlQk5ODlQqlbdD6TV/i9nf4gU4Zk/wt3gB/4vZ3+IF/DNm1jc+18GRMcYYY77FL1sWGGOMMeY5nCwwxhhjzClOFhhjjDHmFCcLjDHGGHPKJ5OF1atXIyMjA4GBgQgNDbVbpqysDNOmTUNgYCCioqLw2muvwWQyOb1ubW0t5s6dC41Gg9DQUMyfPx9NTU1uuAPg4MGDEATB7nb8+HGH5z3wwAPdyr/wwgtuifFmQ4YM6fbaa9ascXpOW1sbsrOzER4ejuDgYMyaNQvV1dUeiffy5cuYP38+EhISEBAQgKFDhyInJ6fHWdY8XcebN2/GkCFDoFarkZaWhmPHjjktv3v3biQnJ0OtVmP06NH4+uuv3RZbV7m5uRg/fjxCQkIQFRWFmTNn4sKFC07P2b59e7e69OTENW+//Xa3109OTnZ6jrfqt5O995kgCMjOzrZb3tN1fPjwYUyfPh16vR6CIODzzz+3OU5EeOuttxATE4OAgABkZmbi4sWLPV63r+8D5lt8MlkwGAyYPXs2XnzxRbvHzWYzpk2bBoPBgPz8fHz00UfYvn073nrrLafXnTt3LoqKirB//3589dVXOHz4MBYsWOCOW0BGRgYqKytttueeew4JCQm4++67nZ77/PPP25y3du1at8Roz6pVq2xee9GiRU7LL1myBHv27MHu3btx6NAhVFRU4PHHH/dIrOfPn4ckSfjggw9QVFSEDRs2YMuWLXjjjTd6PNdTdfzXv/4VS5cuRU5ODr777juMHTsWU6ZMQU1Njd3y+fn5yMrKwvz581FYWIiZM2di5syZOHPmjFvi6+rQoUPIzs7G0aNHsX//fhiNRjz88MNobm52ep5Go7Gpy9LSUrfH2lVKSorN6+fl5Tks68367XT8+HGbePfv3w8AmD17tsNzPFnHzc3NGDt2LDZv3mz3+Nq1a/GnP/0JW7ZsQUFBAYKCgjBlyhS0tbU5vGZf3wfMB5EP27ZtG2m12m77v/76axJFkaqqqqz73n//fdJoNNTe3m73WmfPniUAdPz4ceu+f/7znyQIAl29etXlsd/MYDBQZGQkrVq1ymm5SZMm0auvvur2eOyJj4+nDRs29Lp8XV0dKRQK2r17t3XfuXPnCAAdOXLEDRH2bO3atZSQkOC0jCfreMKECZSdnW393mw2k16vp9zcXLvlf/Ob39C0adNs9qWlpdHChQvdGqc9NTU1BIAOHTrksIyj96in5OTk0NixY3td3pfqt9Orr75KQ4cOJUmS7B73Zh0DoM8++8z6vSRJpNPp6L333rPuq6urI5VKRTt37nR4nb6+D5jv8cmWhZ4cOXIEo0ePRnR0tHXflClT0NDQgKKiIofnhIaG2vxWn5mZCVEUUVBQ4PaYv/zyS9y4cQPz5s3rsezHH3+MiIgIjBo1CitWrEBLS4vb4+u0Zs0ahIeHIzU1Fe+9957TRzsnT56E0WhEZmamdV9ycjLi4uJw5MgRT4TbTX19PcLCwnos54k6NhgMOHnypE39iKKIzMxMh/Vz5MgRm/KA5WfbG/VZX18PAD3WZ1NTE+Lj4xEbG4sZM2Y4fA+6y8WLF6HX65GYmIi5c+eirKzMYVlfql/A8jOyY8cOPPvssw4XzgO8X8edSkpKUFVVZVOHWq0WaWlpDuvwVt4HzPf43EJSvVFVVWWTKACwfu9oCdGqqipERUXZ7JPL5QgLC3PZsqPObN26FVOmTOlxkawnn3wS8fHx0Ov1OH36NF5//XVcuHABf//7390e4yuvvII777wTYWFhyM/Px4oVK1BZWYn169fbLV9VVQWlUtmtX0l0dLRH6vRmxcXF2LRpE9atW+e0nKfq+Pr16zCbzXZ/Vs+fP2/3HEc/256uT0mSsHjxYtx7770YNWqUw3JJSUn4y1/+gjFjxqC+vh7r1q1DRkYGioqKer0gXH+kpaVh+/btSEpKQmVlJVauXImJEyfizJkzCAkJ6VbeV+q30+eff466ujr89re/dVjG23XcVWc99aUOb+V9wHyPx5KF5cuX491333Va5ty5cz12TvK2W7mP8vJy7Nu3D7t27erx+l37UIwePRoxMTGYPHkyLl26hKFDh7o13qVLl1r3jRkzBkqlEgsXLkRubq5Hp3G9lTq+evUqHnnkEcyePRvPP/+803NdXce3o+zsbJw5c8bp838ASE9PR3p6uvX7jIwMjBw5Eh988AHeeecdd4eJqVOnWr8eM2YM0tLSEB8fj127dmH+/Pluf/3+2rp1K6ZOnQq9Xu+wjLfrmDHAg8nCsmXLnGbPAJCYmNira+l0um49aTt74Ot0Oofn3NyZxmQyoba21uE59tzKfWzbtg3h4eH41a9+1evX6ZSWlgbA8lvzrXyQ9afe09LSYDKZcPnyZSQlJXU7rtPpYDAYUFdXZ9O6UF1d3ac67W/MFRUVePDBB5GRkYEPP/ywz6/X3zp2JCIiAjKZrNvoEGf1o9Pp+lTeHV5++WVrB+C+/uaqUCiQmpqK4uJiN0XnXGhoKEaMGOHw9X2hfjuVlpbiwIEDfW7R8mYdd9ZTdXU1YmJirPurq6sxbtw4u+fcyvuA+SBvd5pwpqcOjtXV1dZ9H3zwAWk0Gmpra7N7rc4OjidOnLDu27dvn9s7OEqSRAkJCbRs2bJbOj8vL48A0Pfff+/iyHq2Y8cOEkWRamtr7R7v7OD4t7/9zbrv/PnzHu3gWF5eTsOHD6cnnniCTCbTLV3DnXU8YcIEevnll63fm81mGjx4sNMOjo8++qjNvvT0dI90wJMkibKzs0mv19OPP/54S9cwmUyUlJRES5YscXF0vdPY2EiDBg2iP/7xj3aPe7N+b5aTk0M6nY6MRmOfzvNkHcNBB8d169ZZ99XX1/eqg2Nf3gfM9/hkslBaWkqFhYW0cuVKCg4OpsLCQiosLKTGxkYisrxZRo0aRQ8//DCdOnWK9u7dS5GRkbRixQrrNQoKCigpKYnKy8ut+x555BFKTU2lgoICysvLo+HDh1NWVpZb7+XAgQMEgM6dO9ftWHl5OSUlJVFBQQERERUXF9OqVavoxIkTVFJSQl988QUlJibS/fff79YYiYjy8/Npw4YNdOrUKbp06RLt2LGDIiMj6emnn3YYLxHRCy+8QHFxcfTvf/+bTpw4Qenp6ZSenu72eDvjGTZsGE2ePJnKy8upsrLSujmK2dN1/Omnn5JKpaLt27fT2bNnacGCBRQaGmodyfPUU0/R8uXLreW//fZbksvltG7dOjp37hzl5OSQQqGgH374wS3xdfXiiy+SVqulgwcP2tRlS0uLtczN8a5cuZL27dtHly5dopMnT9ITTzxBarWaioqK3B4vEdGyZcvo4MGDVFJSQt9++y1lZmZSREQE1dTU2I3Xm/Xbldlspri4OHr99de7HfN2HTc2Nlr/zwVA69evp8LCQiotLSUiojVr1lBoaCh98cUXdPr0aZoxYwYlJCRQa2ur9RoPPfQQbdq0yfp9T+8D5vt8Mll45plnCEC37ZtvvrGWuXz5Mk2dOpUCAgIoIiKCli1bZpOhf/PNNwSASkpKrPtu3LhBWVlZFBwcTBqNhubNm2dNQNwlKyuLMjIy7B4rKSmxua+ysjK6//77KSwsjFQqFQ0bNoxee+01qq+vd2uMREQnT56ktLQ00mq1pFaraeTIkfSHP/zBpqXm5niJiFpbW+mll16iQYMGUWBgID322GM2H9butG3bNrs/J10bzHyhjjdt2kRxcXGkVCppwoQJdPToUeuxSZMm0TPPPGNTfteuXTRixAhSKpWUkpJC//jHP9wWW1eO6nLbtm0O4128eLH13qKjo+mXv/wlfffddx6Jl4hozpw5FBMTQ0qlkgYPHkxz5syh4uJih/ESea9+u9q3bx8BoAsXLnQ75u067vy/8+atMyZJkujNN9+k6OhoUqlUNHny5G73ER8fTzk5OTb7nL0PmO/jJaoZY4wx5pRfzrPAGGOMMc/hZIExxhhjTnGywBhjjDGnOFlgjDHGmFOcLDDGGGPMKU4WGGOMMeYUJwuMMcYYc4qTBcYYY4w5xckCY4wxxpziZIExxhhjTnGywBhjjDGnOFlgjDHGmFP/D9Ovs+1TH4PjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    feats_reduced[:, 0],\n",
    "    feats_reduced[:, 1],\n",
    "    c=unnormalize_labels(labels).squeeze(),\n",
    "    cmap=\"magma\",\n",
    ")\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  }
 ],
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   "end_time": "2024-11-09T23:05:42.322136",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
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